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Speaker 1: Hi everyone, Sophia Bush here. Welcome to Work in Progress,

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Speaker 1: where I talk to people who inspire me about how

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Speaker 1: they got to where they are and where they think

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Speaker 1: they're still going. Hi everyone, welcome to another special episode

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Speaker 1: of Work in Progress where we will be continuing our

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Speaker 1: video series Need to Know. Each week, I'll be sitting

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Speaker 1: down with experts to get the answers to your most

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Speaker 1: pressing questions about what is going on in the world

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Speaker 1: right now and what we need to know about this pandemic.

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Speaker 1: I am thrilled to introduce you to my guest today.

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Speaker 1: She is often known as Raven the science Maven. She's

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Speaker 1: a woman who continues to crush boundaries placed on people

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Speaker 1: in the STEM field, particularly marginalized populations. She is the brilliant,

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Speaker 1: multitalented Raven Baxter. Raven is an nationally acclaimed science communicator

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Speaker 1: and molecular biologist who works to advance the state of

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Speaker 1: science culture by creating spaces that are inclusive, educational, and real.

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Speaker 1: Raven creates relatable in engaging digital media in the form

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Speaker 1: of science themed rap music videos and social media as

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Speaker 1: a way to teach science online and foster important conversations

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Speaker 1: about STEM culture. She's an inspiration for young, underrepresented students

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Speaker 1: to pursue careers in science, technology, engineering, and math in

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Speaker 1: case you were wondering what STEM stands for, and she

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Speaker 1: is the founder of Stembassy, a science advocacy organization and

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Speaker 1: web series. Raven was also recognized in this year's Fortune

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Speaker 1: Magazines forty forty and is currently completing a pH d

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Speaker 1: in science education, studying the relationship between how the media's

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Speaker 1: representation of scientists impacts how adults identify with science. In

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Speaker 1: my conversation with Raven, we discuss her childhood, her experiences

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Speaker 1: as a corporate scientist, under representation in higher education and STEM,

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Speaker 1: the current state of science culture, the mutation of the

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Speaker 1: COVID nineteen virus and what we need to know about it,

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Speaker 1: and the importance of taking certain safety measures, plus many

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Speaker 1: of her upcoming projects. Enjoy Raven the science may have

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Speaker 1: ben back there. I'm so excited to have you on

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Speaker 1: the podcast today. Um, everyone at home. I have to

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Speaker 1: just fully confess so I can get it out of

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Speaker 1: the way and we can move on. When when Raven

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Speaker 1: and I got on Zoom today, I was like, um, hey,

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Speaker 1: I'm having a real moment. I have watched to wipe

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Speaker 1: it down like seven hundred and sixty or four times.

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Speaker 1: I think you are a legend, and I I fan

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Speaker 1: boyd really hard. So um, I'm really really jazzed that

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Speaker 1: we're going to have a conversation about all things science

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Speaker 1: and STEM and COVID and the world today. Thank you

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Speaker 1: so much for being here, Thank you for having me, Sophia.

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Speaker 1: I'm so glad to be here, even more excited to

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Speaker 1: know you're enjoying my content and can't wait to talk. Yes,

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Speaker 1: I am. I was just so excited when when I

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Speaker 1: was sent your work. My first sort of thought was like,

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Speaker 1: oh my god, she's like she's this rap version like

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Speaker 1: powerhouse Woman, you know, strong black female Bill ny almost.

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Speaker 1: And then when I was reading your bio, you were

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Speaker 1: talking about how um you started creating all your content

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Speaker 1: as a play on Bill and I as as a

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Speaker 1: response to a lack of culturally relevant material that's actually

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Speaker 1: engaging often underrepresented communities and STEM, and I was just like,

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Speaker 1: she's my hero. I can't I have to meet her

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Speaker 1: at some point, And now we're meeting on Zoom and

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Speaker 1: I'm just really stoked about it. Thank you so much.

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Speaker 1: So I gosh, I want to get into your your

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Speaker 1: whole career. Would you actually mind? Will you introduce yourself

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Speaker 1: to the audience so they have a little understanding of

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Speaker 1: your background, and then I'm I'm actually gonna take us

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Speaker 1: in reverse and go backwards before we catch up to

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Speaker 1: where we are today. Okay, sure. So my name is

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Speaker 1: Raven Baxter. I am from Buffalo, New York, and i

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Speaker 1: am a molecular biologist and science communicator, and I'm currently

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Speaker 1: finishing a pH d in science education. And will that

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Speaker 1: be like your hundredth degree? Read? Honestly, I was reading

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Speaker 1: your bio. I was like, cool, cool, she wentto college

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Speaker 1: is sixteen. That's great, That's really super impressive. And I

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Speaker 1: feel like you've just amassed this incredible collection of degrees

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Speaker 1: and specialized studies. Do you feel like, as a scientist

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Speaker 1: you're going to be a student forever? Yeah? I feel

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Speaker 1: like as a scientist we are always asking question students

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Speaker 1: and exploring the world around us. And I think for

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Speaker 1: me personally, it was always important to just keep trying

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Speaker 1: new things. And I wasn't always successful at everything I tried,

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Speaker 1: and I didn't always like everything that I tried, Like,

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Speaker 1: for example, I went to space camp wanting to be

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Speaker 1: an astronaut and then found out I was afraid of

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Speaker 1: heights at space camp. So but the important thing for

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Speaker 1: me is that like I put myself out there, and

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Speaker 1: I try new things, and I get new perspectives and

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Speaker 1: keep growing as a person. So I mean, I think

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Speaker 1: scientists as a whole we will always be students, um.

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Speaker 1: But I think for me personally, I always want to

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Speaker 1: grow as a person. M M. Yeah. I Interestingly, I

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Speaker 1: really feel like I relate to that. I think as

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Speaker 1: a kid who always loved science, and and even as

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Speaker 1: an adult, there's aspects of my day job that in

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Speaker 1: a way require social science work. You know, you have

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Speaker 1: to really investigate human behavior and the way that we function.

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Speaker 1: And and I think about even now in a time

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Speaker 1: like this, looking at how the world is reacting to

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Speaker 1: a pandemic and also how the virus itself behaves. We're

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Speaker 1: looking at both human behavior and at viral behavior, at

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Speaker 1: cellular behavior, and it's it's pretty interesting to realize that

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Speaker 1: even across the scientific and emotional spectrums, all things are

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Speaker 1: often so connected. Absolutely, yes, I mean we could look

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Speaker 1: at that from many angles. Obviously, if you don't believe

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Speaker 1: in science, you know, if you don't connect with science

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Speaker 1: at a personal level, then it's hard for you to

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Speaker 1: receive advice from scientists even though they're trying to protect

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Speaker 1: you and help you protect yourself. And I mean, on

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Speaker 1: the other hand, we also know that stressed people are

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Speaker 1: more susceptible to certain diseases, So we have to pay

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Speaker 1: close attention to our mental health so that we can

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Speaker 1: make sure that we stay healthy. So yeah, like, just

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Speaker 1: like you said, our the sociological implications of science, and

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Speaker 1: like our bodies are very connected in a lot of

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Speaker 1: different ways. So coming to this place today where you

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Speaker 1: have many letters after your name, degree after degree, title

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Speaker 1: after title, where you have released an album which includes

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Speaker 1: my favorite science song of all time, Wipe It Down,

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Speaker 1: which I mentioned earlier, where you are writing a children's

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Speaker 1: book and consulting on so many other projects, where you

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Speaker 1: have been named to this Forbes list of powerhouse folks,

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Speaker 1: including Beyonce Casual. How how did we get here? How

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Speaker 1: how did Raven, the little girl who grew up in

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Speaker 1: Buffalo forge this path? Can can you take me back

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Speaker 1: to your childhood, to to eight or maybe ten years

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Speaker 1: old and talk to me about where you found yourself.

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Speaker 1: Were you always into science? You know you said, you

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Speaker 1: went to space camp. Did you know from a young age.

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Speaker 1: I'm so curious about about who you were as a

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Speaker 1: little kid. Uh, baby Raven, he was a character. So

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Speaker 1: for um, So, I think that I came out of

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Speaker 1: the womb as a scientist, to be honest. And there

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Speaker 1: there was no point in time where I looked at

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Speaker 1: myself and said, oh, I should be a scientist. That

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Speaker 1: was always just my natural, um tendency to ask questions

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Speaker 1: about the world around me. And maybe when I was

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Speaker 1: about seven or eight, um, I Well, first I should

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Speaker 1: preface this by saying I learned how to read when

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Speaker 1: I was three years old. I was reading books, so

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Speaker 1: I had been, you know, really hands on learning and

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Speaker 1: actually reading about the world around me. So by the

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Speaker 1: time I was seven, I was like, well, gee, you know,

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Speaker 1: I don't want to read about mother goose. I want

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Speaker 1: to read the dictionary. I want to read the encyclopedia. Um.

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Speaker 1: But I started looking at the sky and asking questions

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Speaker 1: about clouds, and I said, oh, you know, there's all

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Speaker 1: these different types of clouds in the sky. And I

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Speaker 1: read books about clouds and learned that there were different

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Speaker 1: names for clouds. Clouds make different types of weather. Um,

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Speaker 1: and all of these different things, and I kind of

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Speaker 1: started going down that rabbit hole. UM. Eventually I learned

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Speaker 1: everything I could about clouds and then I said, oh,

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Speaker 1: what about outer space? That's past the clouds. And that's

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Speaker 1: how I got into going to space camp and UM.

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Speaker 1: But like I said earlier, I found out I was

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Speaker 1: afraid of heights of space Camp. But one thing that

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Speaker 1: I did learn at space Camp was about all of

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Speaker 1: the different careers that it takes to get someone into space.

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Speaker 1: You need geologists, chemists, physicists, um, biologists, you need gosh, engineers,

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Speaker 1: computer people. All of these different career paths have to

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Speaker 1: come into play when you're working in space industry and

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Speaker 1: to get someone in. So even though I couldn't go

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Speaker 1: to space because I would probably have a mental breakdown,

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Speaker 1: I still had all of these other career paths to

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Speaker 1: explore and that pretty much just sent me on my

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Speaker 1: journey to where I am now as a as a biologist.

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Speaker 1: M M. So when you had that kind of aha

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Speaker 1: moment at space Camp, you realize, Okay, going up there's

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Speaker 1: not going to be for me, but but I still

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Speaker 1: want to work in this arena. Was there a particular

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Speaker 1: type of science when you mentioned, you know, physicists and

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Speaker 1: chemists and engineers, and the list goes on. Was there

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Speaker 1: something that stood out to you as a kid or

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Speaker 1: was it more this idea of I know, I'm going

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Speaker 1: to grow up to be the woman in the white

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Speaker 1: lab coat who is discovering things about the world and innovating.

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Speaker 1: Was it specific or more general but but clear in direction? Well, So,

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Speaker 1: what happened after I came back from space camp and

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Speaker 1: and realized that my dreams were gonna get shattered? Um?

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Speaker 1: I started asking questions of like, well, what does the

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Speaker 1: Earth need? Right? Like, it's cool that we could put

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Speaker 1: people into space, and I'm afraid of going to space.

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Speaker 1: And honestly, like, if I'm going to be doing something,

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Speaker 1: I want to be having the most fun. And I

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Speaker 1: feel like the biggest fun for me personally, I think

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Speaker 1: the most fun part about like working at NASA and

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Speaker 1: playing a role in that organization is going to space,

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Speaker 1: even though there's a ton of other career. So I

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Speaker 1: was like, if I'm not going to space, I don't

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Speaker 1: want to do it. So I came home and I'm like, well,

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Speaker 1: what does the Earth need? If I'm not leaving the Earth,

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Speaker 1: then let's figure out what can I do to help

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Speaker 1: the Earth on on the planet. And I started learning

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Speaker 1: about climate change, and I started learning about um global

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Speaker 1: warming and also like just there were a lot of things,

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Speaker 1: a lot of problems that we had to solve here

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Speaker 1: and planet Earth that I felt like needed to be

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Speaker 1: solved before we went out into space. And um so,

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Speaker 1: I got really deep into environmental science and I actually

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Speaker 1: ended up going to college. I started college at the

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Speaker 1: State University of New York College of Environmental Science and

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Speaker 1: Forestry as an environmental law and policy major. Um so,

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Speaker 1: pretty much after Space Camp, I was about thirteen years old.

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Speaker 1: Up until I went to college, I was heavily interested

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Speaker 1: in environmental sciences, which is very different from what I

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Speaker 1: do today. So again I had a lot more of

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Speaker 1: finding my way and more journeys, uh to to have

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Speaker 1: after that period of time as well, for sure. And

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Speaker 1: And what was Buffalo like then? Was it a great

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Speaker 1: place to grow up as a kid? Did did you

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Speaker 1: and your family love being there or were you kind

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Speaker 1: of itching to get to, you know, a bigger city.

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Speaker 1: What what was that element for you? Um So, I

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Speaker 1: was born in Buffalo, and I didn't stay in Buffalo

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Speaker 1: for much of my childhood, I moved around a lot,

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Speaker 1: actually went to about ten different schools when I was

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Speaker 1: a kid. Um. So I came back to Buffalo in

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Speaker 1: time for high school and I wanted to just stay

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Speaker 1: right because I had been around all over the place.

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Speaker 1: My childhood was all over the place, and I really

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Speaker 1: had this itching to stay actually and just get my

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Speaker 1: roots and stay put um. And I was actually able

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Speaker 1: to do that. So I'm glad. That's amazing. And now

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Speaker 1: you've actually been tapped by the City of Buffalo. You're

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Speaker 1: doing these social media takeovers of the mayor's Instagram account

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Speaker 1: and challenging young folks who are in the city to

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Speaker 1: stay home and practice social distancing, and and every week

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Speaker 1: features different challenges. And I just think it must be

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Speaker 1: so cool to to be in this position now, to

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Speaker 1: be talking to the teens in your community because you

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Speaker 1: were once a teenager there. And it is that kind

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Speaker 1: of a surreal moment when when you look at you

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Speaker 1: know how far you've come and and to your point,

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Speaker 1: how life sort of led you in that in that

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Speaker 1: cycle to to get back home and really be rooted

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Speaker 1: in in your city. Yeah, you know, And I've never

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Speaker 1: really looked into it like that, but you're absolutely right. Um,

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Speaker 1: it is so heartwarming to see things come full circle.

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Speaker 1: And UM, I think one of the things that I

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Speaker 1: love the most about this is understanding that anybody can

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Speaker 1: work hard to make a connection with their city and

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Speaker 1: people who have power to influence change and create laws

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Speaker 1: and new rules that can help to protect people in

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Speaker 1: the city and also to promote education and knowledge for

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Speaker 1: citizens in the city and beyond even for for bigger institutions.

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Speaker 1: So I was really excited and glad that they reached

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Speaker 1: out to me because I thought it was so cool

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Speaker 1: that they wanted to spread science to the city and

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Speaker 1: perhaps someone come in and engage the citizens of Buffalo.

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Speaker 1: M Well, I have to say, as a person who's

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Speaker 1: not a scientist by training, who always loved science in

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Speaker 1: school and who is still quite fascinated with science, with

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Speaker 1: the universe, space, all all of the sort of stem technologies,

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Speaker 1: really I'm really really inspired by folks uniting behind science

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Speaker 1: because when I think about human history, when I think

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Speaker 1: about innovation, when I think about how we all got here,

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Speaker 1: every good thing that exists on Earth exists because of

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Speaker 1: science and because of scientific research and scientific discovery, and

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Speaker 1: it's such a strange time to watch science be so

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Speaker 1: under attack and to be able to do the research

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Speaker 1: and discover that that this has been a very intentional

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Speaker 1: attack program, that it was launched because of scientific findings

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Speaker 1: about climate change, about big polluter industries, that there has

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Speaker 1: been an active disinformation campaign against science in order to

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Speaker 1: maintain a certain power structure which is dangerous for the

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Speaker 1: Earth and thus dangerous for all of us. And it

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Speaker 1: doesn't matter, by the way, if the all of us

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Speaker 1: is conservative or liberal, or votes one way or another

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Speaker 1: or independent, it doesn't matter. It's it's what's true on

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Speaker 1: the planet for one of us is true for all

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Speaker 1: of us eventually. And so I am really excited when

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Speaker 1: I see initiatives like the one you're taking part in,

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Speaker 1: when a city says, hey, science is very cool and

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Speaker 1: actually quite important and we need to catch up, like

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Speaker 1: we need to make up for some lost ground here

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Speaker 1: and remind people that the are worthy pursuits. You know.

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Speaker 1: I'm I always find myself kind of giggling that people

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Speaker 1: think my job is so cool. You know, Yes, being

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Speaker 1: a storyteller is great, and I I really love it,

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Speaker 1: and I get nerdy on you know, pre uh on

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Speaker 1: the on the human history pre the written word. You

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Speaker 1: know that we passed things down through generations and cultures

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Speaker 1: around the world, you know, through performance, Like I I

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Speaker 1: get again like nerdy kid over here. But to me,

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Speaker 1: working in my in my world, like you guys are

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Speaker 1: the real rock stars. Scientists to me are like the

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Speaker 1: coolest people on planet Earth. Working on healthcare during the

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Speaker 1: Obama administration, I got to meet Bill n I, the

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Speaker 1: science guy, at the White House and I started screaming.

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Speaker 1: I was like, oh my god, and everybody was like,

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Speaker 1: are you okay? But it's like I just I really

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Speaker 1: like my face is turning round. I'm so glad this

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Speaker 1: is a podcast not a video. I am so jazzed

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Speaker 1: about the the work you do and and anything I

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Speaker 1: can ever do to you, know, help you or support you.

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Speaker 1: I'm I'm so here for thank you so much. That

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Speaker 1: means the world to me. Seriously, when you think about

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Speaker 1: all that you've achieved in your career, you know, becoming

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Speaker 1: a molecular scientist, your many degrees studying the field of biology,

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Speaker 1: you're creating culturally responsive programs and stem by the age

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Speaker 1: of and getting a pH d in science education at

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Speaker 1: and you know, a doctoral degree from the graduate School

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Speaker 1: of Education. Like I, I look at your bio and

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Speaker 1: I could read it for the next hour and not

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Speaker 1: be through. What do you think for you? Feels like

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Speaker 1: the core? What's what's the thing that keeps you excite? How?

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Speaker 1: How did you identify biology as your as your most

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Speaker 1: favorite part of science? And and then what has taken

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Speaker 1: you forward to where we meet today? So when I

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Speaker 1: got to college, right, my story is kind of all

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Speaker 1: over the place in many different ways. But um, I

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Speaker 1: started college when I was sixteen. I was very young.

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Speaker 1: I did not do well because I'm very social, and

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Speaker 1: I was able to start college so early because I

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Speaker 1: was academically gifted in high school. And um, but I

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Speaker 1: didn't have to work for it, right, I didn't have

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Speaker 1: to put a lot of effort into getting good grades.

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Speaker 1: I just showed up and did well. And that does

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Speaker 1: not work when you go to college. You actually have

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Speaker 1: to put a lot of effort into what you're doing.

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Speaker 1: And so although I made a lot of friends my

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Speaker 1: firstman year of college as a young sixteen year old,

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Speaker 1: I was also very social and did not have any

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Speaker 1: study plans. I did not really understand you know what

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Speaker 1: that what that life was supposed to look like, and

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Speaker 1: so I actually failed out of college. I failed out

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Speaker 1: of college because I could not keep my grades up

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Speaker 1: and I had to come home, and I attended a

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Speaker 1: community college. And um, I, you know, because of a

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Speaker 1: lot of reasons, I was very motivated to get out

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Speaker 1: of the house. There was a lot of things happening

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Speaker 1: at home that I felt like I needed to kind

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Speaker 1: of figure out how to, you know, get out of

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Speaker 1: the house and go back to school. And so I

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Speaker 1: attended a community college and got my grades up enough

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Speaker 1: to transfer out. And so when I got to the

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Speaker 1: place where I eventually graduated from Buffalo State, UM, I

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Speaker 1: was still interested in environmental science, but I took a

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Speaker 1: genetics class because it was a requirement for my major.

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Speaker 1: And when I took genetics, I I realized that I

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Speaker 1: was learning a language. And I had already already speak

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Speaker 1: too languages. I speak English and Spanish, and so as

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Speaker 1: I was learning genetics, I thought, huh, everything about this

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Speaker 1: is like a language. There's an actual sentence structure to

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Speaker 1: our genes. If if you aren't aware, UM, our genes

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Speaker 1: have like beginnings, middles, and ends right to the way

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Speaker 1: that they're coded. There are promoter regions, which is where

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Speaker 1: all the proteins come and gather to get ready to

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Speaker 1: read the gene. And then there's the gene itself that

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Speaker 1: calls for the information and how to make a new protein.

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Speaker 1: And then there's the end, which is like a terminator sequence,

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Speaker 1: and it's an actual sequence that says stop here. It's

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Speaker 1: like a period. So when I learned that, I was

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Speaker 1: super fascinated, Like, gosh, all of us, everybody in the world,

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Speaker 1: not just humans, all living organisms speak this language, right, Um,

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Speaker 1: but we're all not aware of it. I don't think

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Speaker 1: everybody is aware that that we are also similar like this.

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Speaker 1: And UM, I went down that rabbit hole and I

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Speaker 1: never came out. That is where my love for biology

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Speaker 1: really started. UM, particularly my love for molecular biology, which

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Speaker 1: is something that genetics falls under. Um. Just studying these

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Speaker 1: little components, these molecules that helped to generate our life processes,

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Speaker 1: and UM really connects us all is human beings. That's

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Speaker 1: so beautiful. Thanks. I love that our genes are like sentences,

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Speaker 1: they speak a language. I'm like swooning over here. And

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Speaker 1: I think it's so important. You know, even what you

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Speaker 1: touch on about your story, resiliency and support systems. I

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Speaker 1: couldn't have gone to college at sixteen and had my

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Speaker 1: ship together. There's no way, you know, you don't. You

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Speaker 1: don't know how to do anything yet. And yeah, you were,

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Speaker 1: you were a kid. And what I think is actually

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Speaker 1: really incredible about that is is the sort of precipice

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Speaker 1: you stood on of having this incredible academic prowess. You know,

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Speaker 1: as you mentioned, you excelled at school and got really

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Speaker 1: good grades without much effort because you know, you have

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Speaker 1: a specific kind of brain, and and yet you still

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Speaker 1: had to sort of hurdle the social experience. And I

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Speaker 1: think I've come to be so fascinated by studying resiliency,

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Speaker 1: and I think being able to say, you know, I

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Speaker 1: went from being a college dropout to a PhD scientist

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Speaker 1: is so important to acknowledge. You know. We we live

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Speaker 1: in this culture where failure is so often shunned, but

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Speaker 1: failure is actually a building block to success. And I

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Speaker 1: think if we could all own, you know, all all

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Speaker 1: of our stories in those ways, we would actually get

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Speaker 1: through that stuff. We'd get through our learning. I think

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Speaker 1: more quickly and more holistically. Absolutely, I always tell people

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Speaker 1: don't be afraid to fail, and also don't be afraid

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Speaker 1: to talk about your failures. I think a lot of

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Speaker 1: people like to take their failure secret and tucked away inside,

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Speaker 1: and this image that we're perfect and we don't have

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Speaker 1: to do that. So getting to today being a scientist

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Speaker 1: and a science communicator and making an album and writing

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Speaker 1: a children's book, what what would you say your day

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Speaker 1: in and day out experience is now? What are you studying?

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Speaker 1: Um and and what led you to decide that you

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Speaker 1: needed to start communicating science in in such an approachable

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Speaker 1: way for people? Um So, I think that throughout my life,

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Speaker 1: I've always done science my way, learned science my way,

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Speaker 1: and taught science my way and as my unapologetic self.

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Speaker 1: And that has not always been easy. I've as a

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Speaker 1: as a student, it's always went relatively well for me.

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Speaker 1: I was never challenged because I just wanted to be

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Speaker 1: myself and do science. But as a professional, UM I

402
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Speaker 1: had many roadblocks. I had many times where people simply

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Speaker 1: just didn't think I belonged in the space as a scientist,

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Speaker 1: or that I was a token, or that gosh, you know,

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Speaker 1: but I didn't look like a scientist and all of

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Speaker 1: these all of these things were things that I was

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Speaker 1: experiencing as a professional, and I realized that we had

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Speaker 1: some real issues in science culture that we needed to address, UM.

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Speaker 1: And so that is where I began to take up

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Speaker 1: space as a science communicator because I wanted to begin

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Speaker 1: sending messages out into the world that literally anybody can

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Speaker 1: do science. And by anybody, I mean like actually like

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Speaker 1: any physical being. Right. You can be big, small, tall, short, purple, green, black, white,

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Speaker 1: like you know from Antarctica from Texas. Anybody can use science, young, old,

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Speaker 1: and everything in between. And UM. That was that was

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Speaker 1: the messaging that I that I had originally intended to

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Speaker 1: share on my platform. And as time went on, especially

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Speaker 1: as the pandemic started, I thought it was incredibly important

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Speaker 1: to use my platform to communicate actual science, um, and

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Speaker 1: not just about the diversity that's needed in science and UM.

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Speaker 1: So that's where Wipe it Down came from. And I

422
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Speaker 1: had done some other songs and some other music videos

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Speaker 1: ire to that, but that was really my first time saying, hey, guys,

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Speaker 1: you know what, I'm going to be very different. Okay,

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Speaker 1: you probably don't see scientists wrapping, dancing, singing on the internet,

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Speaker 1: but I need to give you this information about COVID

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Speaker 1: nineteen and I need you to remember it, and I

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Speaker 1: want you to listen and also not be afraid, because

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Speaker 1: we're going through a very scary time right now, and

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Speaker 1: I'm just here to make you comfortable and to teach

431
00:27:27,920 --> 00:27:32,520
Speaker 1: you about the disease. Mm hmm. And I think you're

432
00:27:32,600 --> 00:27:40,120
Speaker 1: right making things feel approachable, and also the humor through

433
00:27:40,160 --> 00:27:43,000
Speaker 1: which you do that makes it a little less scary,

434
00:27:43,119 --> 00:27:46,960
Speaker 1: because it is an incredibly scary time. But information is

435
00:27:47,000 --> 00:27:51,240
Speaker 1: actually power. Information can remove some of the fear and

436
00:27:51,240 --> 00:27:54,960
Speaker 1: and give us the tools to stay safe and prioritize

437
00:27:55,000 --> 00:27:58,679
Speaker 1: care for ourselves and our communities. And being able to

438
00:27:58,760 --> 00:28:03,080
Speaker 1: do that in a way that also creates joy is

439
00:28:03,160 --> 00:28:14,040
Speaker 1: something that as a viewer, I'm incredibly thankful for, if

440
00:28:14,040 --> 00:28:16,760
Speaker 1: you don't mind me asking, because you know you're you

441
00:28:16,880 --> 00:28:24,159
Speaker 1: refer to it a bit, the the roadblocks that began

442
00:28:24,359 --> 00:28:27,600
Speaker 1: popping up for you, you know, in the early days

443
00:28:27,640 --> 00:28:31,240
Speaker 1: of your career as as a professional scientist. And it's

444
00:28:31,240 --> 00:28:34,119
Speaker 1: not lost on me that all of this social science

445
00:28:34,200 --> 00:28:37,160
Speaker 1: data says that there's really not many women in STEM,

446
00:28:37,160 --> 00:28:40,000
Speaker 1: and there are not many people of color and STEM,

447
00:28:40,040 --> 00:28:44,360
Speaker 1: and so sitting at the intersection of being a black woman,

448
00:28:44,960 --> 00:28:49,440
Speaker 1: I'm I'm curious can can you let some of the

449
00:28:49,480 --> 00:28:52,000
Speaker 1: folks who are listening at home in on a bit

450
00:28:52,880 --> 00:28:55,800
Speaker 1: of what that experience is like. And I do want

451
00:28:55,840 --> 00:28:58,400
Speaker 1: to be clear, I never want anyone who comes on

452
00:28:58,440 --> 00:29:02,440
Speaker 1: this show to have to, you know, relive any version

453
00:29:02,480 --> 00:29:07,520
Speaker 1: of of trauma. And also I want people who listen

454
00:29:07,560 --> 00:29:09,920
Speaker 1: who may not have had your experience or who may

455
00:29:09,920 --> 00:29:12,240
Speaker 1: not have a friend with your experience, to to be

456
00:29:12,400 --> 00:29:16,280
Speaker 1: enlightened a little bit. So I say both of those

457
00:29:16,280 --> 00:29:19,360
Speaker 1: things only to say that there's no expectation of you know,

458
00:29:19,520 --> 00:29:22,280
Speaker 1: you having to like share horror stories. But if there's

459
00:29:22,280 --> 00:29:24,200
Speaker 1: anything that you think could be a teaching moment for

460
00:29:24,240 --> 00:29:27,200
Speaker 1: anyone that that is comfortable for you to to share

461
00:29:27,240 --> 00:29:31,479
Speaker 1: with us, I I would love to, you know, offer

462
00:29:31,600 --> 00:29:36,880
Speaker 1: an audience to to witness your experience, sir um and

463
00:29:36,960 --> 00:29:42,840
Speaker 1: thank you for that. I I've had as a student,

464
00:29:43,760 --> 00:29:48,360
Speaker 1: like I said earlier, phenomenal experiences in science, and I've

465
00:29:48,480 --> 00:29:55,320
Speaker 1: I've had mentors, advisers, professors, you know, friendships that were

466
00:29:55,440 --> 00:30:04,840
Speaker 1: very um, beneficial and loving and accepting. And so when

467
00:30:04,880 --> 00:30:08,880
Speaker 1: it came time for me to work outside of the

468
00:30:08,880 --> 00:30:13,240
Speaker 1: academic setting as a professional and in a corporate setting, UM,

469
00:30:13,280 --> 00:30:16,080
Speaker 1: I was expecting that I was going to have those

470
00:30:16,160 --> 00:30:22,480
Speaker 1: same experiences. And in terms of my race and ethnicity,

471
00:30:22,520 --> 00:30:26,480
Speaker 1: I had always existed in predominantly white environments. I was

472
00:30:26,560 --> 00:30:31,680
Speaker 1: raised in in Williamsville, UM like Clarence, New York, which

473
00:30:31,760 --> 00:30:38,000
Speaker 1: is very white. UM. I went to predominantly white colleges

474
00:30:38,280 --> 00:30:46,240
Speaker 1: and universities, and I had never had any issues with discrimination,

475
00:30:47,200 --> 00:30:52,480
Speaker 1: with people being unfriendly. So, you know, it was very

476
00:30:52,520 --> 00:30:56,000
Speaker 1: shocking to me the way that I was being treated

477
00:30:56,160 --> 00:31:00,200
Speaker 1: as a professional. Because you I thought that, okay, were

478
00:31:00,240 --> 00:31:02,960
Speaker 1: all adults right by this time in our lives, we

479
00:31:03,000 --> 00:31:05,719
Speaker 1: all should really understand how to treat each other and

480
00:31:05,760 --> 00:31:08,680
Speaker 1: how to care for one another. But I wasn't having

481
00:31:08,880 --> 00:31:13,400
Speaker 1: a very caring and loving experience. UM. So I was

482
00:31:13,480 --> 00:31:21,920
Speaker 1: the only black woman scientist in my laboratory, and I

483
00:31:21,920 --> 00:31:24,680
Speaker 1: did my best. You know, I love science, and I

484
00:31:24,760 --> 00:31:27,640
Speaker 1: was there because I was qualified, and I was passionate

485
00:31:28,200 --> 00:31:34,600
Speaker 1: about doing science, and I came every day excited about

486
00:31:34,640 --> 00:31:38,400
Speaker 1: my job. But it really wasn't lost on me that

487
00:31:39,640 --> 00:31:43,840
Speaker 1: although I was doing my job, I was very lonely. UM.

488
00:31:43,920 --> 00:31:48,640
Speaker 1: I would be in my lab doing experiments and working hard,

489
00:31:49,400 --> 00:31:52,680
Speaker 1: and there would be other people in the lab talking

490
00:31:52,680 --> 00:31:57,800
Speaker 1: to each other. UM. Giving. You know, they were caring

491
00:31:57,840 --> 00:32:00,240
Speaker 1: for each other. They were having small talk and aiding

492
00:32:00,280 --> 00:32:03,080
Speaker 1: each other. Hey, you know, let's go, let's get to

493
00:32:03,080 --> 00:32:05,280
Speaker 1: know each other outside of work. I care about you.

494
00:32:05,400 --> 00:32:08,000
Speaker 1: What are you doing today? Right? Like? How was your family?

495
00:32:08,320 --> 00:32:11,959
Speaker 1: Little things like that. Um. I felt like everybody was

496
00:32:12,000 --> 00:32:14,480
Speaker 1: going the extra mile to get to know each other,

497
00:32:14,680 --> 00:32:17,800
Speaker 1: but not me. I wasn't being included, and that was

498
00:32:18,600 --> 00:32:21,280
Speaker 1: that sucked because, as I said before, I'm super social

499
00:32:21,360 --> 00:32:23,400
Speaker 1: and like I don't have a mean bone in my body.

500
00:32:23,480 --> 00:32:26,600
Speaker 1: I was trying to, like, you know, make sure that

501
00:32:26,640 --> 00:32:29,240
Speaker 1: people knew that I was approachable. I was bringing in cupcakes,

502
00:32:29,400 --> 00:32:33,800
Speaker 1: I was, I was doing a lot. I was really trying, like, hey,

503
00:32:33,840 --> 00:32:36,520
Speaker 1: maybe these people don't understand that I want to be friends.

504
00:32:36,560 --> 00:32:40,120
Speaker 1: Let me bring cupcakes. That didn't even work. So, you know,

505
00:32:41,240 --> 00:32:44,080
Speaker 1: even though that happened, I was still trying my best,

506
00:32:44,240 --> 00:32:48,760
Speaker 1: still doing my job doing science. And eventually they did

507
00:32:48,880 --> 00:32:54,960
Speaker 1: hire another um My lab hired another black person at

508
00:32:55,080 --> 00:32:57,840
Speaker 1: my job, and I said, Okay, maybe they'll talk to me,

509
00:32:58,760 --> 00:33:03,440
Speaker 1: and they did. Um, but I didn't get a chance

510
00:33:03,440 --> 00:33:05,400
Speaker 1: to really work with them because they were the custodian,

511
00:33:05,600 --> 00:33:09,720
Speaker 1: So they didn't hire a black scientist. Um. But also

512
00:33:10,000 --> 00:33:13,800
Speaker 1: one of my co workers turned to me that day

513
00:33:13,800 --> 00:33:16,520
Speaker 1: that this person was hired and she said, well, you

514
00:33:16,520 --> 00:33:22,760
Speaker 1: should be happy because you won't be the token black anymore. Wow. Yeah,

515
00:33:22,800 --> 00:33:27,120
Speaker 1: that's what she said. And so you know, in that moment,

516
00:33:27,360 --> 00:33:29,880
Speaker 1: it was it was a huge moment of realization for

517
00:33:29,920 --> 00:33:35,120
Speaker 1: me because I my worst fear was confirmed, and that

518
00:33:35,640 --> 00:33:38,120
Speaker 1: even though I was showing up to work every day

519
00:33:38,240 --> 00:33:41,240
Speaker 1: and I was super excited about being a scientist and

520
00:33:41,280 --> 00:33:45,000
Speaker 1: like I had been a scientist at heart from childhood

521
00:33:45,040 --> 00:33:48,640
Speaker 1: and I had gotten my degrees right, and I was

522
00:33:48,760 --> 00:33:52,320
Speaker 1: hired like I was qualified. Every day I walked into

523
00:33:52,400 --> 00:33:56,480
Speaker 1: the building, this person and perhaps other people were just

524
00:33:56,520 --> 00:33:59,400
Speaker 1: looking at me as a token and that nothing that

525
00:33:59,480 --> 00:34:03,239
Speaker 1: I had accomplished or achieved to get here mattered, and

526
00:34:03,280 --> 00:34:07,960
Speaker 1: that I was just there for nothing, basically to keep

527
00:34:08,040 --> 00:34:13,640
Speaker 1: up an image. And that was very, very painful for me. Yeah.

528
00:34:13,920 --> 00:34:17,839
Speaker 1: So yeah, I don't she didn't say that, I don't

529
00:34:17,880 --> 00:34:20,440
Speaker 1: think from a bad place in her heart. But the

530
00:34:20,480 --> 00:34:22,839
Speaker 1: issue was that she thought it was okay to say that,

531
00:34:23,560 --> 00:34:27,120
Speaker 1: and she wasn't really socially aware to say, oh, you know,

532
00:34:27,400 --> 00:34:30,200
Speaker 1: I should not say this to Raven. That's not nice

533
00:34:30,719 --> 00:34:33,880
Speaker 1: that would make her feel bad. UM and I but

534
00:34:33,920 --> 00:34:36,480
Speaker 1: I also wasn't socially aware to stop her and say, hey,

535
00:34:36,560 --> 00:34:41,240
Speaker 1: you hurt my feelings. Please don't say that, you know. UM.

536
00:34:41,320 --> 00:34:45,280
Speaker 1: So there were other things that had happened that were

537
00:34:45,360 --> 00:34:50,080
Speaker 1: on that same caliber UM of just discrimination and her

538
00:34:50,120 --> 00:34:54,279
Speaker 1: full experiences that ultimately made me leave UM. But I

539
00:34:54,360 --> 00:34:58,400
Speaker 1: ended up going into education, and you know, although I

540
00:34:58,400 --> 00:35:01,600
Speaker 1: still had some weird experiences as an educator, overall it

541
00:35:01,680 --> 00:35:05,920
Speaker 1: was a very rewarding experience. And I now teach to

542
00:35:06,160 --> 00:35:10,280
Speaker 1: whoever i'm speaking to UM or whoever i'm educating about

543
00:35:10,360 --> 00:35:13,920
Speaker 1: social awareness, cultural awareness, and how important it is to

544
00:35:14,719 --> 00:35:20,080
Speaker 1: UM to understand diversity, inclusion, and and equity so that

545
00:35:20,120 --> 00:35:23,280
Speaker 1: you can create a sense of belonging for other people

546
00:35:23,400 --> 00:35:25,799
Speaker 1: who are who you're working with and who you're doing

547
00:35:25,840 --> 00:35:31,040
Speaker 1: science with. Because ultimately, if we can't retain minorities and science,

548
00:35:31,640 --> 00:35:33,959
Speaker 1: then we're not We're never going to really be able

549
00:35:33,960 --> 00:35:39,040
Speaker 1: to achieve diversity because we won't be able to maintain numbers, right. UM.

550
00:35:39,080 --> 00:35:41,600
Speaker 1: So that's it's very important, and we need we need

551
00:35:41,640 --> 00:35:45,799
Speaker 1: diversity to be able to make to do good science. Absolutely,

552
00:35:46,800 --> 00:35:48,920
Speaker 1: I think about it, you know, I obviously have not

553
00:35:49,000 --> 00:35:53,600
Speaker 1: experienced discrimination as a black woman, but I've gone through

554
00:35:53,600 --> 00:35:56,040
Speaker 1: it on a gender basis. I know what it's like

555
00:35:56,120 --> 00:35:59,920
Speaker 1: to be the one woman in the room. And I

556
00:36:00,160 --> 00:36:04,000
Speaker 1: heard something said a few years ago. This is going

557
00:36:04,040 --> 00:36:05,920
Speaker 1: to sound round about, but I promise I'll get to

558
00:36:05,920 --> 00:36:10,800
Speaker 1: a point. So I listened to Oprah interviewing this Catholic

559
00:36:10,920 --> 00:36:15,239
Speaker 1: nun on Super Soul sunday Um Sister Joan. She's like

560
00:36:15,400 --> 00:36:19,640
Speaker 1: ninety two years old. And my mom grew up uh

561
00:36:19,680 --> 00:36:22,520
Speaker 1: in the Catholic Church and didn't have great experiences. So

562
00:36:22,560 --> 00:36:25,719
Speaker 1: I think I've always been a little wary of of

563
00:36:25,760 --> 00:36:29,120
Speaker 1: the rigidity of of that particular kind of organized religion.

564
00:36:29,640 --> 00:36:31,480
Speaker 1: And I really wanted to know, you know, what the

565
00:36:31,520 --> 00:36:34,120
Speaker 1: conversation was going to be like between Oprah and this woman,

566
00:36:34,160 --> 00:36:36,799
Speaker 1: because I grew up, you know, much in the way

567
00:36:36,840 --> 00:36:39,000
Speaker 1: that you were obsessed with science, I was really obsessed

568
00:36:39,040 --> 00:36:40,680
Speaker 1: with language. And I used to beg my mom to

569
00:36:40,680 --> 00:36:43,160
Speaker 1: pick me up fifteen minutes early from school so I

570
00:36:43,160 --> 00:36:45,239
Speaker 1: could get home for the start of the Oprah Winfrey Show.

571
00:36:45,239 --> 00:36:47,280
Speaker 1: And my mom was like, that is not my responsibility

572
00:36:47,280 --> 00:36:50,120
Speaker 1: as your parents. My responsibility is to send you to school,

573
00:36:50,120 --> 00:36:52,040
Speaker 1: Like what is wrong with you, and I was like,

574
00:36:52,120 --> 00:36:55,360
Speaker 1: she is a journalist and I just love her. So anyway,

575
00:36:55,360 --> 00:36:59,880
Speaker 1: a long story longer. Oprah's interviewing this woman and she's

576
00:37:00,040 --> 00:37:05,680
Speaker 1: talking about how gender parity is her ultimate goal within

577
00:37:05,800 --> 00:37:10,719
Speaker 1: religious institutions, which you wouldn't think about in the Catholic Church,

578
00:37:11,480 --> 00:37:16,400
Speaker 1: and she said, I don't believe What did she say?

579
00:37:16,560 --> 00:37:18,920
Speaker 1: She said, I believe that so many of the problems

580
00:37:18,920 --> 00:37:21,600
Speaker 1: in the world come from the fact that people with

581
00:37:21,680 --> 00:37:25,000
Speaker 1: fifty of the information are making a percent of the decisions.

582
00:37:25,360 --> 00:37:28,240
Speaker 1: She was obviously talking about that on a traditional gender line,

583
00:37:28,760 --> 00:37:33,640
Speaker 1: you know, discussing how men have half the information and

584
00:37:33,680 --> 00:37:36,279
Speaker 1: women the other fifty one percent of the population have

585
00:37:36,360 --> 00:37:38,759
Speaker 1: the other half, and that we would actually be making

586
00:37:38,760 --> 00:37:43,160
Speaker 1: holistic decisions if both people's opinions and experiences were equally weighted.

587
00:37:43,719 --> 00:37:46,640
Speaker 1: And I thought, when I heard that, that's absolutely true,

588
00:37:46,680 --> 00:37:48,680
Speaker 1: you know, I felt it in my bones. And then

589
00:37:48,719 --> 00:37:54,120
Speaker 1: I thought about what that exact same conversation about real

590
00:37:54,200 --> 00:37:57,719
Speaker 1: equity means when you move outside of gender and you

591
00:37:57,800 --> 00:38:00,680
Speaker 1: move into culture, and you move across race, and you

592
00:38:00,760 --> 00:38:06,600
Speaker 1: move across experience, and you move into you know, gender identity,

593
00:38:06,640 --> 00:38:09,520
Speaker 1: sexual identity, preference, all of these things and if everyone

594
00:38:09,640 --> 00:38:12,040
Speaker 1: was allowed to really come to the table and be

595
00:38:12,160 --> 00:38:15,719
Speaker 1: celebrated for the validity of their experience, all of our

596
00:38:15,800 --> 00:38:18,719
Speaker 1: solutions would be better. And I don't think it would

597
00:38:18,760 --> 00:38:21,600
Speaker 1: take us as long to get two really good answers

598
00:38:22,480 --> 00:38:27,480
Speaker 1: and and so. On the one hand, I hate that

599
00:38:27,560 --> 00:38:30,320
Speaker 1: you experienced that, and on the other I'm so glad

600
00:38:30,360 --> 00:38:33,560
Speaker 1: that you didn't let it take you out of science completely,

601
00:38:33,640 --> 00:38:36,560
Speaker 1: and that you've chosen to actually go out there and

602
00:38:36,600 --> 00:38:40,760
Speaker 1: be such a public facing scientist and communicator and teach

603
00:38:40,800 --> 00:38:44,920
Speaker 1: other people and actually diversify the field so that you

604
00:38:44,960 --> 00:38:50,080
Speaker 1: can create more inclusion. I think it's really badass, and

605
00:38:50,160 --> 00:38:54,120
Speaker 1: I'm glad that you stayed. Thank you, Thank you so much.

606
00:38:54,640 --> 00:38:58,480
Speaker 1: I mean that that Oprah story is hilarious because I

607
00:38:58,560 --> 00:39:01,360
Speaker 1: also used to run home off the school bus so

608
00:39:01,400 --> 00:39:02,640
Speaker 1: I can catch it. I think it came on at

609
00:39:02,680 --> 00:39:05,279
Speaker 1: like four PM or something, so it came on at

610
00:39:05,320 --> 00:39:08,160
Speaker 1: three on the West Coast, and I was like, please

611
00:39:08,200 --> 00:39:09,839
Speaker 1: pick me up early, and my mom was like, who

612
00:39:09,880 --> 00:39:12,920
Speaker 1: are what is the matter with you? Um? You know,

613
00:39:12,960 --> 00:39:14,960
Speaker 1: I was like, you know, eight years old and my

614
00:39:15,000 --> 00:39:18,319
Speaker 1: two favorite TV shows were Oprah and Murphy Brown, and

615
00:39:18,360 --> 00:39:21,120
Speaker 1: my mom was kind of like Okay, I don't know.

616
00:39:21,280 --> 00:39:23,440
Speaker 1: All the other kids are like watching cartoons. I had

617
00:39:23,480 --> 00:39:27,440
Speaker 1: no interest. I suppose it makes sense that I wound

618
00:39:27,480 --> 00:39:31,120
Speaker 1: up with a podcast later in life. Yes, I mean

619
00:39:31,239 --> 00:39:34,040
Speaker 1: I will go to say that, like, although I have

620
00:39:34,320 --> 00:39:40,279
Speaker 1: had negative experiences, I'm very optimistic. And I also like

621
00:39:40,400 --> 00:39:44,560
Speaker 1: to say my haters are my motivators, and not to

622
00:39:44,560 --> 00:39:47,480
Speaker 1: say that people hate me. You know, I don't know

623
00:39:47,920 --> 00:39:51,080
Speaker 1: why that would even be a possibility, but like I

624
00:39:51,200 --> 00:39:54,160
Speaker 1: use haters as a term for people who have told

625
00:39:54,200 --> 00:39:58,320
Speaker 1: me no, or people who have denied me the opportunity

626
00:39:58,400 --> 00:40:01,879
Speaker 1: to pursue my goals, either by you know, oppressing me

627
00:40:02,239 --> 00:40:07,040
Speaker 1: or by directly telling me no. Um. I always am like, okay,

628
00:40:07,080 --> 00:40:09,239
Speaker 1: so you don't think I can do this, Well, guess

629
00:40:09,280 --> 00:40:12,680
Speaker 1: what I'm gonna do it? You know you don't think right,

630
00:40:12,760 --> 00:40:15,440
Speaker 1: like you're gonna have to actually be a scientist on

631
00:40:15,560 --> 00:40:17,880
Speaker 1: TV now, Like I'm sorry. You know it's because you

632
00:40:17,880 --> 00:40:21,279
Speaker 1: told me I couldn't do it. Yeah, I love that,

633
00:40:21,680 --> 00:40:25,120
Speaker 1: and it's interesting I read in one of one of

634
00:40:25,160 --> 00:40:27,279
Speaker 1: the pieces I read about you, you you said that my

635
00:40:27,320 --> 00:40:30,239
Speaker 1: biggest piece of advice for my students is always be

636
00:40:30,320 --> 00:40:33,040
Speaker 1: your own biggest advocate and push yourself to do things

637
00:40:33,080 --> 00:40:37,080
Speaker 1: that challenge you. And I think exactly that when there's

638
00:40:37,120 --> 00:40:41,400
Speaker 1: a roadblock to encourage people, you know, it's on you

639
00:40:41,440 --> 00:40:43,560
Speaker 1: to figure out how to climb over it, jump over it,

640
00:40:43,680 --> 00:40:45,680
Speaker 1: drive over it. You know you can, but you can

641
00:40:45,719 --> 00:40:52,320
Speaker 1: do it absolutely. I mean I I've been a student,

642
00:40:52,440 --> 00:40:56,120
Speaker 1: I've been a professor, I've been an academic advisor, I've

643
00:40:56,160 --> 00:41:01,160
Speaker 1: been a researcher, and in all of those capacities, just

644
00:41:01,239 --> 00:41:03,960
Speaker 1: based on my own experiences and then also mes sing

645
00:41:03,960 --> 00:41:09,279
Speaker 1: what other people experience. I've never seen anybody suffer from

646
00:41:09,320 --> 00:41:13,719
Speaker 1: like pushing past adversity to get to where they ultimately

647
00:41:13,760 --> 00:41:17,680
Speaker 1: want to go. And so I always advise people to

648
00:41:17,840 --> 00:41:21,200
Speaker 1: just if it challenges you, that just means you're experiencing

649
00:41:21,320 --> 00:41:25,239
Speaker 1: something that is a growth opportunity. You might not have

650
00:41:25,440 --> 00:41:30,600
Speaker 1: like the the mental or like emotional capacity to quite

651
00:41:30,640 --> 00:41:34,440
Speaker 1: handle it, but you can gain that by powering through

652
00:41:34,719 --> 00:41:37,720
Speaker 1: and getting to the other side, and then your possibilities

653
00:41:37,760 --> 00:41:42,040
Speaker 1: are endless. Right well, And when when we zoom out

654
00:41:42,080 --> 00:41:43,960
Speaker 1: a little bit, you know, it's great advice on a

655
00:41:44,160 --> 00:41:46,800
Speaker 1: on an individual's experience, but when we zoom out to

656
00:41:46,840 --> 00:41:50,520
Speaker 1: the actual field of science, when we think about stem fields,

657
00:41:50,520 --> 00:41:57,840
Speaker 1: in general, closing gaps in health disparities really requires getting

658
00:41:57,840 --> 00:42:01,480
Speaker 1: over hurdles like that. It really requires errs, as you've said,

659
00:42:01,680 --> 00:42:07,799
Speaker 1: culturally responsive science communication. So in identifying the lack of

660
00:42:07,840 --> 00:42:11,520
Speaker 1: that type of communication, is that what motivated you to

661
00:42:11,600 --> 00:42:17,000
Speaker 1: become an educator, to launch a YouTube channel, to to

662
00:42:17,000 --> 00:42:19,920
Speaker 1: to do your own version of a of a Bill

663
00:42:20,080 --> 00:42:24,239
Speaker 1: n I program. What was it that that made that

664
00:42:24,560 --> 00:42:29,399
Speaker 1: switch flip? Oh? So I know from a young age

665
00:42:29,440 --> 00:42:31,520
Speaker 1: that this is what I wanted to do, and I

666
00:42:31,560 --> 00:42:36,239
Speaker 1: think there is one point in my past where I

667
00:42:36,280 --> 00:42:40,680
Speaker 1: can say I really made this choice, but I didn't

668
00:42:40,719 --> 00:42:42,680
Speaker 1: know how I was going to get there. And that

669
00:42:42,880 --> 00:42:47,040
Speaker 1: was in a conversation with one of my academic advisors.

670
00:42:47,120 --> 00:42:52,239
Speaker 1: His name is Dr Potts, and I was, you know,

671
00:42:52,600 --> 00:42:54,520
Speaker 1: at a crossroads. I wasn't sure if I wanted to

672
00:42:54,560 --> 00:42:56,600
Speaker 1: go to medical school. I wasn't sure if I wanted

673
00:42:56,640 --> 00:43:00,239
Speaker 1: to like be a veterinarian or like a teacher. I

674
00:43:00,280 --> 00:43:02,760
Speaker 1: wasn't sure. I just knew I wanted to do science

675
00:43:02,760 --> 00:43:05,760
Speaker 1: in some capacity. And so he set me down, He said, well, Raven,

676
00:43:06,520 --> 00:43:09,440
Speaker 1: what do you want to do? Like, just forget titles,

677
00:43:09,800 --> 00:43:13,759
Speaker 1: if you could describe your perfect day, what would that be,

678
00:43:15,640 --> 00:43:19,960
Speaker 1: and so I said that I want to do science

679
00:43:20,880 --> 00:43:27,200
Speaker 1: and I want to wear cute shoes. So that's that's

680
00:43:27,200 --> 00:43:29,239
Speaker 1: what I That's all I had. And I was being

681
00:43:29,239 --> 00:43:31,360
Speaker 1: real honest, I was like, Dr Pods, I want to

682
00:43:31,360 --> 00:43:33,480
Speaker 1: wear cute shoes and I want to do science. That's

683
00:43:33,520 --> 00:43:36,560
Speaker 1: all I got. I'm not really sure, you know. And

684
00:43:36,640 --> 00:43:40,560
Speaker 1: so I didn't think much of what I said at

685
00:43:40,600 --> 00:43:43,680
Speaker 1: that point, because I mean, I was I was being transparent,

686
00:43:43,800 --> 00:43:45,920
Speaker 1: and I think he thought it was joking. But the

687
00:43:45,960 --> 00:43:49,920
Speaker 1: reality is that's what I wanted, right And in other words,

688
00:43:50,040 --> 00:43:52,799
Speaker 1: I guess, in an abstract way, me doing science and

689
00:43:52,840 --> 00:43:55,120
Speaker 1: wearing cute shoes is another way of saying I want

690
00:43:55,160 --> 00:43:57,680
Speaker 1: to do science and be myself and come as I

691
00:43:57,719 --> 00:44:01,800
Speaker 1: am and not have to worry out what society thinks

692
00:44:01,880 --> 00:44:04,960
Speaker 1: a scientists should do what look like. So I eventually

693
00:44:04,960 --> 00:44:08,600
Speaker 1: got a master's degree, I worked in corporate I started

694
00:44:08,800 --> 00:44:13,640
Speaker 1: teaching at a community college, and you know, as I

695
00:44:13,680 --> 00:44:16,439
Speaker 1: was educating people, I saw how excited they were just

696
00:44:16,440 --> 00:44:18,759
Speaker 1: just see me in the classroom. Like one of my

697
00:44:18,880 --> 00:44:22,319
Speaker 1: favorite uh days were the first days of school, first

698
00:44:22,400 --> 00:44:25,359
Speaker 1: day of lecture, where I think I was twenty four,

699
00:44:25,719 --> 00:44:28,200
Speaker 1: twenty four at the time where I was working as

700
00:44:28,239 --> 00:44:32,799
Speaker 1: a professor, and I, um, all of the students would

701
00:44:32,840 --> 00:44:36,120
Speaker 1: be seated in in their seating area, and I would

702
00:44:36,120 --> 00:44:38,600
Speaker 1: walk up to the podium and they'd be like, well,

703
00:44:38,640 --> 00:44:42,280
Speaker 1: who are you Why are you standing there? And I said,

704
00:44:42,640 --> 00:44:48,120
Speaker 1: I'm your professor. They would be like, holy crap, like

705
00:44:48,560 --> 00:44:53,680
Speaker 1: fat jaws dropping, people are smiling. Um. And I taught

706
00:44:53,680 --> 00:44:56,840
Speaker 1: in a really diverse college, a community college. I actually

707
00:44:56,840 --> 00:44:59,839
Speaker 1: had people that were grandmothers, you know, above the age

708
00:44:59,840 --> 00:45:05,160
Speaker 1: of sixty in groups with recent high school graduates like sixteen, seventeen,

709
00:45:05,200 --> 00:45:08,280
Speaker 1: eighteen year old working together. And Buffalo has a large

710
00:45:08,320 --> 00:45:13,400
Speaker 1: refugee population and immigrant population. UM. So they were just

711
00:45:13,440 --> 00:45:17,200
Speaker 1: a diversity and age and ethnicity and race and gender

712
00:45:17,239 --> 00:45:21,360
Speaker 1: and um, all of these things in the classroom. But

713
00:45:21,400 --> 00:45:24,440
Speaker 1: they were all no matter where they were from their background,

714
00:45:24,440 --> 00:45:27,960
Speaker 1: they were all excited to see me, even though if

715
00:45:28,000 --> 00:45:29,799
Speaker 1: it was the first day of school and they didn't

716
00:45:29,840 --> 00:45:31,880
Speaker 1: even know anything about me. They were just happy to

717
00:45:31,920 --> 00:45:36,640
Speaker 1: see me in the classroom. And so, you know, knowing

718
00:45:36,760 --> 00:45:40,440
Speaker 1: with all of my experiences and knowing how important representation

719
00:45:40,640 --> 00:45:45,120
Speaker 1: is to a lot of people, UM, I really set

720
00:45:45,160 --> 00:45:48,280
Speaker 1: myself on a path to just being my unapologetic self

721
00:45:48,560 --> 00:45:54,000
Speaker 1: as a public um teaching scientists and science communicator um

722
00:45:54,200 --> 00:45:57,880
Speaker 1: and making sure that in everything I do, I am

723
00:45:57,920 --> 00:46:02,000
Speaker 1: making people aware of of science and also like giving

724
00:46:02,400 --> 00:46:07,680
Speaker 1: giving a are setting a new image of what a

725
00:46:07,760 --> 00:46:10,520
Speaker 1: scientist looks like in American and redefining what that means

726
00:46:10,600 --> 00:46:16,600
Speaker 1: for for everybody. When you think about redefining what a

727
00:46:16,680 --> 00:46:20,200
Speaker 1: scientist means, I think about permission. Really. I think about

728
00:46:20,719 --> 00:46:24,040
Speaker 1: how many of us, in our own individual experiences are

729
00:46:24,080 --> 00:46:28,320
Speaker 1: clamoring for more doors to be opened and more seats

730
00:46:28,360 --> 00:46:32,840
Speaker 1: to be pulled up to even larger tables. And I'm

731
00:46:32,840 --> 00:46:36,920
Speaker 1: curious what that feels like now, because we talked about

732
00:46:36,960 --> 00:46:39,759
Speaker 1: that a bit at the beginning. We're in a very

733
00:46:39,800 --> 00:46:44,879
Speaker 1: strange time. The the administration that's in charge of our

734
00:46:44,920 --> 00:46:51,680
Speaker 1: country right now is incredibly vicious to a lot of

735
00:46:51,719 --> 00:46:56,560
Speaker 1: the kinds of communities we're talking about. It is unapologetically

736
00:46:56,800 --> 00:47:00,640
Speaker 1: attacking science left and right. And that's how happening in

737
00:47:00,680 --> 00:47:04,200
Speaker 1: the midst of both a global pandemic, which for some

738
00:47:04,320 --> 00:47:08,040
Speaker 1: reason the administration wants to act like is a hoax

739
00:47:08,080 --> 00:47:10,200
Speaker 1: against them, And I think, Wow, what a what an

740
00:47:10,320 --> 00:47:12,879
Speaker 1: entitled thing to think that something that's affecting the whole

741
00:47:12,920 --> 00:47:17,320
Speaker 1: planet was a setup against this one guy who's an idiot.

742
00:47:17,400 --> 00:47:21,920
Speaker 1: I don't know part of my frankness, but we we

743
00:47:22,040 --> 00:47:26,360
Speaker 1: look at that. We're looking at both an incredibly tense

744
00:47:26,440 --> 00:47:31,680
Speaker 1: time culturally, scientifically, environmentally. You know, the entire West coast

745
00:47:31,719 --> 00:47:34,279
Speaker 1: of the United States is on fire right now and

746
00:47:34,520 --> 00:47:36,200
Speaker 1: at the time that you and I are recording this.

747
00:47:37,200 --> 00:47:42,520
Speaker 1: What role do you think that politics plays in science

748
00:47:42,520 --> 00:47:47,600
Speaker 1: and what role should science play in politics? Mm hmm, Well,

749
00:47:47,760 --> 00:47:50,239
Speaker 1: I don't think that politics should play a role in

750
00:47:50,280 --> 00:47:54,480
Speaker 1: science at all. I think they need to be separate completely.

751
00:47:56,040 --> 00:48:00,120
Speaker 1: There is nothing, um there's I mean, science should not

752
00:48:00,200 --> 00:48:05,160
Speaker 1: be political. I mean we don't need politics to do science.

753
00:48:05,760 --> 00:48:09,000
Speaker 1: And that's that's just how I feel. It makes things

754
00:48:09,120 --> 00:48:13,720
Speaker 1: way too complicated and and just difficult when you start

755
00:48:13,960 --> 00:48:17,160
Speaker 1: adding in these dimensions that don't need to be there.

756
00:48:18,000 --> 00:48:23,160
Speaker 1: As as a constituent who values science, thank you for that.

757
00:48:23,239 --> 00:48:27,800
Speaker 1: And I also will say I wish as a voter

758
00:48:28,920 --> 00:48:32,120
Speaker 1: science was actually allowed to influence politics. I do not

759
00:48:32,280 --> 00:48:34,840
Speaker 1: think I agree with you. I do not think politics

760
00:48:34,880 --> 00:48:37,719
Speaker 1: should ever be allowed to influence science. But I do

761
00:48:37,880 --> 00:48:42,560
Speaker 1: think political leaders, regardless of party, should take science seriously.

762
00:48:42,760 --> 00:48:48,719
Speaker 1: To me, science should always be bipartisan and respected. So

763
00:48:49,200 --> 00:48:54,520
Speaker 1: I hope that we can all help in any realm

764
00:48:54,760 --> 00:48:57,480
Speaker 1: and and sort of career vertical. I hope that we

765
00:48:57,520 --> 00:48:59,680
Speaker 1: can all help advocate for all of you in the

766
00:48:59,680 --> 00:49:05,080
Speaker 1: science community better as time moves forward. Absolutely, I think

767
00:49:05,239 --> 00:49:09,319
Speaker 1: one of my first jobs, I think was an internship.

768
00:49:09,400 --> 00:49:12,720
Speaker 1: I interned for an organization that I think is defunct now,

769
00:49:12,800 --> 00:49:17,719
Speaker 1: but they were scientists and engineers for America. And as

770
00:49:17,760 --> 00:49:20,799
Speaker 1: an intern, it was my job to interview political candidates

771
00:49:20,840 --> 00:49:25,160
Speaker 1: and understand what their stances are on different science policies

772
00:49:25,200 --> 00:49:28,759
Speaker 1: and environmental policies. And we had a database where we

773
00:49:28,800 --> 00:49:31,239
Speaker 1: would keep track of all of these things. We would

774
00:49:31,280 --> 00:49:36,239
Speaker 1: interview local candidates and also like national candidates, and that

775
00:49:36,320 --> 00:49:39,520
Speaker 1: was That's been important to me for a very long time.

776
00:49:39,880 --> 00:49:44,200
Speaker 1: And I agree with you. There is something called science

777
00:49:44,239 --> 00:49:47,520
Speaker 1: debate that is a you know how we have the

778
00:49:47,880 --> 00:49:51,880
Speaker 1: presidential debates. Um, there is something called science debate. It's

779
00:49:51,880 --> 00:49:56,759
Speaker 1: obviously not as big as the official presidential debates, but um,

780
00:49:56,880 --> 00:50:01,399
Speaker 1: they are trying to popularize getting politics sans on their

781
00:50:01,440 --> 00:50:04,600
Speaker 1: platform to talk about scientific issues and hold them accountable

782
00:50:04,680 --> 00:50:08,520
Speaker 1: for understanding and being able to communicate their stances on

783
00:50:08,640 --> 00:50:14,400
Speaker 1: scientific policies. I would love that too, Yeah, and I

784
00:50:14,440 --> 00:50:17,239
Speaker 1: think it it really it brings up something that you've

785
00:50:17,280 --> 00:50:21,120
Speaker 1: said before, which really hits home for me, that science

786
00:50:21,280 --> 00:50:24,960
Speaker 1: is relevant to everyday life. It shouldn't just be when

787
00:50:24,960 --> 00:50:29,120
Speaker 1: a pandemic is occurring. You said that the reality is

788
00:50:29,160 --> 00:50:33,160
Speaker 1: we really should be thinking about science every day. How

789
00:50:33,200 --> 00:50:36,799
Speaker 1: are you talking about science in terms of the pandemic

790
00:50:37,239 --> 00:50:41,560
Speaker 1: and perhaps allowing people through this aha moment they're having

791
00:50:41,760 --> 00:50:45,239
Speaker 1: because of the pandemic. How are you bringing more and

792
00:50:45,280 --> 00:50:49,319
Speaker 1: more people to the science table right now? You know,

793
00:50:50,200 --> 00:50:53,120
Speaker 1: I like to talk about science in a way that

794
00:50:53,320 --> 00:50:57,239
Speaker 1: people don't realize that I'm actually having a conversation with

795
00:50:57,280 --> 00:51:03,839
Speaker 1: them about science. It has I've had instances in the

796
00:51:03,880 --> 00:51:07,080
Speaker 1: past where I've spoken to people and they're like, Oh,

797
00:51:07,400 --> 00:51:09,960
Speaker 1: science is not my thing. Just stop right there. I

798
00:51:10,000 --> 00:51:12,160
Speaker 1: don't want to hear it. And then they just like

799
00:51:12,520 --> 00:51:14,759
Speaker 1: hands over their ears. They're like, no, la, la la,

800
00:51:14,880 --> 00:51:17,560
Speaker 1: I can't hear you. Um. You know, don't do the

801
00:51:17,600 --> 00:51:21,360
Speaker 1: science thing. And I don't want that, right, So I

802
00:51:21,440 --> 00:51:27,880
Speaker 1: tried to I basically trick people into listening to me. Um.

803
00:51:28,200 --> 00:51:32,880
Speaker 1: But but having I like to have conversations and instead

804
00:51:32,880 --> 00:51:37,279
Speaker 1: of preaching science. Um, I like to have conversations about

805
00:51:37,480 --> 00:51:40,160
Speaker 1: how are you doing today? You know, did you hear

806
00:51:40,200 --> 00:51:45,520
Speaker 1: about this? Oh? Okay, you know, or sliding in facts

807
00:51:45,680 --> 00:51:48,719
Speaker 1: about oh are you feeling stressed today? That means you know,

808
00:51:48,800 --> 00:51:52,120
Speaker 1: your cortisol levels are going to increase. And guess what

809
00:51:52,200 --> 00:51:55,399
Speaker 1: happens when your cortisol levels increases affects all of these

810
00:51:55,440 --> 00:51:58,040
Speaker 1: different parts of your body and you know, can cause

811
00:51:58,080 --> 00:52:01,000
Speaker 1: these diseases. So let's make sure you're not stressed today.

812
00:52:01,040 --> 00:52:05,400
Speaker 1: And um So, when you start to massage science and

813
00:52:05,440 --> 00:52:07,880
Speaker 1: talk about science in a way that's relatable to people

814
00:52:08,040 --> 00:52:10,279
Speaker 1: on a personal level and what they go through every day,

815
00:52:10,320 --> 00:52:14,719
Speaker 1: it's much easier to insert information about the pandemic and

816
00:52:14,800 --> 00:52:18,480
Speaker 1: like in the coronavirus and about vaccines, which can be

817
00:52:18,480 --> 00:52:23,120
Speaker 1: a very touchy subject for some people. UM So, yeah,

818
00:52:23,160 --> 00:52:27,640
Speaker 1: you have to build communities around science and like build

819
00:52:28,280 --> 00:52:34,480
Speaker 1: conversations into the communities while disseminating scientific information. Why do

820
00:52:34,520 --> 00:52:38,520
Speaker 1: you think so many people I think they can't understand science.

821
00:52:39,200 --> 00:52:42,640
Speaker 1: I don't know, I don't know. It's an integral part

822
00:52:42,680 --> 00:52:48,840
Speaker 1: of our bodies, right, our bodies running on science. Um

823
00:52:48,880 --> 00:52:54,880
Speaker 1: everything is literally everything is science. Everything, I mean everything

824
00:52:54,960 --> 00:52:59,319
Speaker 1: can be studied and and looked at, and you can

825
00:52:59,360 --> 00:53:04,839
Speaker 1: form hYP hypothesis or a question about things around you anything, Um,

826
00:53:04,880 --> 00:53:08,560
Speaker 1: and you can. You can use science to explain many things.

827
00:53:08,600 --> 00:53:12,759
Speaker 1: So I don't know. Um, I think that it's just

828
00:53:12,800 --> 00:53:17,640
Speaker 1: that people don't realize that literally, anything that's going on

829
00:53:17,680 --> 00:53:22,040
Speaker 1: around you can be considered science. Um. I think that

830
00:53:22,160 --> 00:53:25,439
Speaker 1: anybody can be a scientist. You just have to start

831
00:53:25,480 --> 00:53:30,440
Speaker 1: asking questions and and looking into answering them. So I

832
00:53:30,480 --> 00:53:32,640
Speaker 1: don't know why I try. I try to get people

833
00:53:32,680 --> 00:53:35,920
Speaker 1: to realize that they can do science. Anybody can do science.

834
00:53:36,520 --> 00:53:41,520
Speaker 1: I wonder sometimes if part of people's fear or aversion

835
00:53:41,520 --> 00:53:45,000
Speaker 1: to science is that it's in flux. And I think

836
00:53:45,040 --> 00:53:48,200
Speaker 1: that can be really hard for people to understand that

837
00:53:48,200 --> 00:53:54,440
Speaker 1: that so much of science is based on obviously fact,

838
00:53:54,600 --> 00:53:59,320
Speaker 1: but also observation, changing times, changing human behaviors, changing climate.

839
00:54:00,520 --> 00:54:05,640
Speaker 1: Science is steady but also constantly moving, and I think

840
00:54:05,680 --> 00:54:08,120
Speaker 1: the both and can be hard for people to hold

841
00:54:08,719 --> 00:54:12,279
Speaker 1: Why why is science always changing? Can you can you

842
00:54:12,360 --> 00:54:14,719
Speaker 1: break that down for us a little bit and and

843
00:54:14,760 --> 00:54:18,080
Speaker 1: maybe make that feel a little less scary? Um? Yes,

844
00:54:18,280 --> 00:54:22,960
Speaker 1: I can break this down. So let's say, Okay, let's

845
00:54:23,239 --> 00:54:27,279
Speaker 1: put ourselves in a time period. We're no longer. We

846
00:54:27,320 --> 00:54:30,840
Speaker 1: are now in the year seventeen hundred. Right, we're in

847
00:54:30,840 --> 00:54:34,480
Speaker 1: the year sevent dred. Nobody alive can even imagine what

848
00:54:34,520 --> 00:54:38,920
Speaker 1: that's like. But it's nothing like. Okay, we are on horses,

849
00:54:38,960 --> 00:54:42,160
Speaker 1: we're doing horse and carriages. We are doing I don't know,

850
00:54:42,239 --> 00:54:47,560
Speaker 1: carrier pigeons. Life is different. Ok Candles, there's no lighting. Yeah,

851
00:54:48,120 --> 00:54:51,560
Speaker 1: we we're doing candles. We're eating corridge, all right, there's

852
00:54:51,560 --> 00:54:56,279
Speaker 1: no uber eats, there's nothing. Um. But let's say that

853
00:54:56,400 --> 00:55:02,040
Speaker 1: somebody like was really curious about peanut butter and jelly sandwiches.

854
00:55:02,120 --> 00:55:04,520
Speaker 1: Let's say that we have peanut butter and jelly sandwiches

855
00:55:04,560 --> 00:55:09,000
Speaker 1: and seventeen hundred. I don't don't fact check me on this, Okay,

856
00:55:09,000 --> 00:55:12,120
Speaker 1: I don't know, um, but let's say they did exist. Then,

857
00:55:13,280 --> 00:55:17,239
Speaker 1: so let's say the scientists wants to know, okay, how

858
00:55:17,239 --> 00:55:20,040
Speaker 1: many people like peanut butter and jelly sandwiches in the

859
00:55:20,120 --> 00:55:23,640
Speaker 1: year seventeen hundred. They're gonna go around with the p

860
00:55:24,000 --> 00:55:26,520
Speaker 1: P B and j sandwich and ask people yes or

861
00:55:26,560 --> 00:55:29,880
Speaker 1: no do you like peanut butter and jelly, and you're

862
00:55:29,920 --> 00:55:32,799
Speaker 1: gonna get it yes or no answer. Maybe they get

863
00:55:32,800 --> 00:55:38,120
Speaker 1: a percentage of people do like it don't like it? Great,

864
00:55:38,400 --> 00:55:42,120
Speaker 1: we got the data. We know that we're good. Right, Sure,

865
00:55:42,320 --> 00:55:45,040
Speaker 1: we're good for seventeen hundred when we asked that question.

866
00:55:45,400 --> 00:55:48,880
Speaker 1: But maybe let's go to the year eighteen hundred and

867
00:55:48,920 --> 00:55:51,279
Speaker 1: another scientist comes up and says, well, I know that

868
00:55:51,320 --> 00:55:54,880
Speaker 1: we know that sixty for some of people like peanut

869
00:55:54,920 --> 00:55:58,719
Speaker 1: butter and jelly and don't. But do they like it

870
00:55:58,880 --> 00:56:01,399
Speaker 1: on wheat bread or do they like it on rye

871
00:56:01,400 --> 00:56:03,640
Speaker 1: bread or do they like it on pumper nickel bread

872
00:56:04,239 --> 00:56:10,080
Speaker 1: or white bread, right, or honeywheat or twelve grain. We

873
00:56:10,120 --> 00:56:11,920
Speaker 1: don't know. So now we have to go back and

874
00:56:11,960 --> 00:56:14,960
Speaker 1: do the research. And you can imagine how that gets complicated.

875
00:56:14,960 --> 00:56:17,680
Speaker 1: And then maybe fifty years later someone's like, but what

876
00:56:17,760 --> 00:56:21,720
Speaker 1: about crunchy peanut butter? What about the crunchy peanut butter? Right?

877
00:56:22,080 --> 00:56:24,840
Speaker 1: And then fifty years later someone says, well, what about

878
00:56:24,840 --> 00:56:29,359
Speaker 1: concorde jelly versus strawberry jelly? Right? So now we have

879
00:56:29,520 --> 00:56:32,759
Speaker 1: all of this, so much information, right, and if you

880
00:56:32,760 --> 00:56:36,520
Speaker 1: look at it, if you look at the timeline, it's like, dang, well,

881
00:56:36,520 --> 00:56:39,720
Speaker 1: why they said that people like peanut butter and jelly?

882
00:56:39,760 --> 00:56:41,520
Speaker 1: Why didn't it just stay that way. It's because we

883
00:56:41,600 --> 00:56:45,400
Speaker 1: found out about all these new variables. Right, We don't

884
00:56:45,440 --> 00:56:49,160
Speaker 1: know everything there is to know at one given point

885
00:56:49,160 --> 00:56:52,919
Speaker 1: of time. We are constantly asking the same questions over

886
00:56:52,960 --> 00:56:58,360
Speaker 1: and over again, um, in a more specific and intricate way. Also,

887
00:56:58,760 --> 00:57:02,359
Speaker 1: we might have better technology. Right, So in seven, when

888
00:57:02,360 --> 00:57:05,320
Speaker 1: we were sending carrier pigeons out, you know, or sending

889
00:57:05,320 --> 00:57:07,200
Speaker 1: people on foot to go hold up a peanut butter

890
00:57:07,200 --> 00:57:09,480
Speaker 1: and jelly sandwich to your face and ask you if

891
00:57:09,480 --> 00:57:11,520
Speaker 1: you like it or not. You know, now we're near

892
00:57:12,239 --> 00:57:14,520
Speaker 1: we can just send you a picture, right, or maybe

893
00:57:14,560 --> 00:57:17,920
Speaker 1: New Year three thousand, I can send you a hologram,

894
00:57:18,480 --> 00:57:22,080
Speaker 1: and you know you can. I can teleport you peanut

895
00:57:22,080 --> 00:57:26,280
Speaker 1: butter and jelly sandwich. So who knows technology changes or

896
00:57:26,400 --> 00:57:30,200
Speaker 1: questions change, um, But but we could still be asking

897
00:57:30,200 --> 00:57:32,760
Speaker 1: all of these questions and still be investigating the same

898
00:57:32,760 --> 00:57:37,800
Speaker 1: exact thing. Um. So apply that scenario to basically anything

899
00:57:37,840 --> 00:57:41,240
Speaker 1: that can be studied, and that is why science is

900
00:57:41,280 --> 00:57:44,320
Speaker 1: always changing. Hopefully that made sense, No, it really doesn't.

901
00:57:44,400 --> 00:57:49,120
Speaker 1: And in fact, it kind of helps me qualify even

902
00:57:49,160 --> 00:57:53,720
Speaker 1: what's going on with COVID nineteen because it reminds me

903
00:57:53,800 --> 00:57:57,840
Speaker 1: to think about variables and how many variables you all

904
00:57:57,840 --> 00:58:02,160
Speaker 1: in the science community are adding every month, every month,

905
00:58:02,200 --> 00:58:05,800
Speaker 1: every week, all the time where there's new research when

906
00:58:05,840 --> 00:58:08,720
Speaker 1: we think about variables, and lots of folks are asking

907
00:58:08,800 --> 00:58:12,120
Speaker 1: what are the long term effects of COVID? Is a

908
00:58:12,240 --> 00:58:17,040
Speaker 1: lack of those long term variables being recorded part of

909
00:58:17,040 --> 00:58:20,800
Speaker 1: the reason that question is hard to answer. It is

910
00:58:20,800 --> 00:58:23,800
Speaker 1: hard to answer because we can't predict the future. Again,

911
00:58:23,840 --> 00:58:28,560
Speaker 1: this is a novel, it's a new virus. We've never

912
00:58:28,600 --> 00:58:31,800
Speaker 1: seen this before, and therefore we don't know what the

913
00:58:31,840 --> 00:58:34,520
Speaker 1: long term effects are. We can only make predictions based

914
00:58:34,560 --> 00:58:38,400
Speaker 1: on the experiences that people have on the short term

915
00:58:39,040 --> 00:58:41,440
Speaker 1: um but there's really no way for us to truly

916
00:58:41,480 --> 00:58:44,360
Speaker 1: know five or ten years down the line how this

917
00:58:44,440 --> 00:58:48,080
Speaker 1: is really going to impact someone's health, right because nobody

918
00:58:48,120 --> 00:58:51,360
Speaker 1: at this point has had it for five years, it

919
00:58:51,440 --> 00:58:58,920
Speaker 1: didn't exist exactly. Tricky for me, that that makes me

920
00:58:58,920 --> 00:59:01,760
Speaker 1: want to double down on all of my precautions and

921
00:59:01,800 --> 00:59:07,640
Speaker 1: efforts because as because as so many people are, as

922
00:59:07,720 --> 00:59:11,040
Speaker 1: I'm looking at the news and I'm seeing the reports

923
00:59:11,120 --> 00:59:15,160
Speaker 1: of some of the post COVID complications people are suffering from,

924
00:59:15,200 --> 00:59:18,920
Speaker 1: my brain gets really caught up in fear for what's

925
00:59:18,920 --> 00:59:21,520
Speaker 1: going to happen to those people next year, and in

926
00:59:21,600 --> 00:59:23,880
Speaker 1: two years, and in five years and in ten years.

927
00:59:24,200 --> 00:59:26,040
Speaker 1: You know how how rough is this going to be

928
00:59:26,120 --> 00:59:29,680
Speaker 1: for them? And really, what this conversation is making me

929
00:59:29,760 --> 00:59:33,040
Speaker 1: realize is the only real way we can ensure that

930
00:59:33,080 --> 00:59:37,320
Speaker 1: people aren't suffering for incredibly long periods of time from

931
00:59:37,360 --> 00:59:40,040
Speaker 1: something like this is to make sure people don't get it.

932
00:59:41,400 --> 00:59:45,320
Speaker 1: Absolutely all we can really control right now is preventing

933
00:59:45,360 --> 00:59:51,560
Speaker 1: the disease by using our precautions and also by trusting

934
00:59:51,600 --> 00:59:54,560
Speaker 1: science and trusting that people are working hard behind the

935
00:59:54,600 --> 00:59:57,320
Speaker 1: scenes on the front lines to make sure that we

936
00:59:57,480 --> 01:00:00,280
Speaker 1: get a vaccine or some type of medical treat meant

937
01:00:00,680 --> 01:00:04,600
Speaker 1: to help people who have the disease and also um

938
01:00:04,640 --> 01:00:07,760
Speaker 1: to prevent people from getting the disease. As a biologist,

939
01:00:07,840 --> 01:00:11,800
Speaker 1: can you speak a little bit too the safety of vaccines?

940
01:00:11,920 --> 01:00:15,760
Speaker 1: We obviously see a lot of fear, which as a

941
01:00:15,800 --> 01:00:19,040
Speaker 1: person who grew up with a grandfather who UH talked

942
01:00:19,040 --> 01:00:25,280
Speaker 1: about the polio vaccine being, in his estimation, a holy innovation,

943
01:00:25,960 --> 01:00:29,400
Speaker 1: I worry that there's a lot of misinformation out there

944
01:00:29,440 --> 01:00:32,919
Speaker 1: that could actually be detrimental to us beating COVID if

945
01:00:32,920 --> 01:00:36,120
Speaker 1: folks don't get vaccinated, do you, as a scientist, feel

946
01:00:36,120 --> 01:00:40,280
Speaker 1: that you can comfortably speak to the safety of vaccines. Yeah,

947
01:00:40,440 --> 01:00:43,160
Speaker 1: you know, I think a lot of the fear behind

948
01:00:43,240 --> 01:00:47,160
Speaker 1: vaccines comes from people not exactly sure what they are

949
01:00:47,280 --> 01:00:50,439
Speaker 1: or how they work. And I think I can maybe

950
01:00:50,440 --> 01:00:52,800
Speaker 1: break this down a little bit on a basic level

951
01:00:52,840 --> 01:00:56,400
Speaker 1: for folks who are listening. Um, so let's say that

952
01:00:57,360 --> 01:00:59,840
Speaker 1: somebody wanted to give you a food that you would

953
01:00:59,840 --> 01:01:04,280
Speaker 1: never or tried before. So let's say that they wanted

954
01:01:04,320 --> 01:01:09,040
Speaker 1: to give you licorice, right, and you've never had liquorice before,

955
01:01:09,120 --> 01:01:11,520
Speaker 1: so you're like, I don't know if I'm gonna like liquorice.

956
01:01:11,680 --> 01:01:14,520
Speaker 1: So you take a taste, you try and take a

957
01:01:14,560 --> 01:01:18,480
Speaker 1: little nibble, You put it on your tongue, let the

958
01:01:18,560 --> 01:01:23,360
Speaker 1: juices and the flavors flow, and you decide whether you

959
01:01:23,400 --> 01:01:27,120
Speaker 1: like it or not. Getting a vaccine is kind of

960
01:01:27,160 --> 01:01:31,120
Speaker 1: the exact same thing. So vaccines usually there's there's different

961
01:01:31,120 --> 01:01:33,600
Speaker 1: types of vaccines, and they're made in different ways, but

962
01:01:34,680 --> 01:01:38,720
Speaker 1: essentially what a vaccine is is a little piece, it's

963
01:01:38,720 --> 01:01:43,240
Speaker 1: a little taste of, uh, the disease that it is

964
01:01:43,480 --> 01:01:47,240
Speaker 1: trying to protect your body against. And so when we

965
01:01:47,280 --> 01:01:50,560
Speaker 1: get vaccinations, we are giving our bodies just a little

966
01:01:50,600 --> 01:01:55,120
Speaker 1: taste of a disease. It's an inactive taste, right, meaning

967
01:01:55,160 --> 01:01:57,160
Speaker 1: that it's not meant to make you sick. It's just

968
01:01:57,240 --> 01:02:02,320
Speaker 1: like a little portion of it is just to say, hey,

969
01:02:02,360 --> 01:02:05,320
Speaker 1: this is what this is. Do you like it or not? Yes? No,

970
01:02:05,960 --> 01:02:08,480
Speaker 1: And usually our bodies are like, no, we don't like this.

971
01:02:09,640 --> 01:02:13,520
Speaker 1: And um, what our bodies do is we produce antibodies

972
01:02:13,880 --> 01:02:20,240
Speaker 1: to fight against that that disease and so that those

973
01:02:20,280 --> 01:02:23,480
Speaker 1: antibodies will work to protect us from the disease in

974
01:02:23,520 --> 01:02:27,120
Speaker 1: the future. That's what a vaccine is. It's just us

975
01:02:27,240 --> 01:02:31,600
Speaker 1: giving your body just a little preview and giving our

976
01:02:31,640 --> 01:02:34,920
Speaker 1: bodies a chance to decide if it's going to protect

977
01:02:34,960 --> 01:02:38,360
Speaker 1: us or not. Right, And and so this is science

978
01:02:38,440 --> 01:02:42,680
Speaker 1: that has been done for hundreds of years. Vaccines are

979
01:02:42,720 --> 01:02:46,840
Speaker 1: not new. Um. We have been giving vaccines in many

980
01:02:46,880 --> 01:02:53,040
Speaker 1: different ways over hundreds of years. Um. People in older

981
01:02:53,120 --> 01:02:57,120
Speaker 1: civilizations had vaccine technology. They were I mean, if you

982
01:02:57,160 --> 01:03:00,800
Speaker 1: look into it's really interesting. They would take like they

983
01:03:00,800 --> 01:03:04,800
Speaker 1: would take little scabs and rub it on people who

984
01:03:04,880 --> 01:03:08,800
Speaker 1: weren't infected and to protect people from disease. And so

985
01:03:09,840 --> 01:03:12,000
Speaker 1: it's nothing to be afraid of. This is something we've

986
01:03:12,040 --> 01:03:15,400
Speaker 1: researched for a long time. It's not new. We know

987
01:03:15,520 --> 01:03:19,720
Speaker 1: it works. Um, it doesn't always work as well as

988
01:03:19,760 --> 01:03:22,040
Speaker 1: we think it should work, but we do know that

989
01:03:22,080 --> 01:03:25,800
Speaker 1: it plays a major role in protecting UM, protecting us

990
01:03:25,840 --> 01:03:28,640
Speaker 1: as a society. So it's it's the responsible thing to do.

991
01:03:28,680 --> 01:03:31,560
Speaker 1: We're working very hard to make sure that they are

992
01:03:31,680 --> 01:03:34,640
Speaker 1: safe and that they are as effective as possible, and

993
01:03:35,040 --> 01:03:37,880
Speaker 1: don't have a lot of options. You know, we're doing

994
01:03:37,920 --> 01:03:42,240
Speaker 1: all that we can. So I definitely recommend UM getting

995
01:03:42,240 --> 01:03:46,400
Speaker 1: a vaccine, and I will be getting the vaccine, and

996
01:03:46,440 --> 01:03:50,320
Speaker 1: I hope that you do too. I absolutely well as well.

997
01:03:50,960 --> 01:03:55,120
Speaker 1: I think it's also important to remember, as you mentioned,

998
01:03:55,680 --> 01:04:00,520
Speaker 1: we don't know how efficient they'll be. So sometimes vaccines

999
01:04:00,560 --> 01:04:04,280
Speaker 1: have let's say a sixty or a seventy percent success

1000
01:04:04,400 --> 01:04:07,800
Speaker 1: rate at making people immune to an illness, which is

1001
01:04:07,880 --> 01:04:10,560
Speaker 1: part of the reason that it's so important that everyone

1002
01:04:10,680 --> 01:04:13,520
Speaker 1: get them, so that enough people can be immune to

1003
01:04:13,560 --> 01:04:18,840
Speaker 1: the illness that we can stop the spread. Is that right, right? Exactly? So,

1004
01:04:18,880 --> 01:04:21,360
Speaker 1: when we talk about vaccines, one of the things that

1005
01:04:21,400 --> 01:04:26,040
Speaker 1: we've also received some questions about is viral mutation. Will

1006
01:04:26,200 --> 01:04:30,880
Speaker 1: the COVID nineteen virus mutate. Is it mutating? If it does,

1007
01:04:31,000 --> 01:04:36,680
Speaker 1: will a vaccines still be efficient or effective? Can you

1008
01:04:36,720 --> 01:04:41,880
Speaker 1: speak to that. Yeah. So, one of the things about

1009
01:04:42,000 --> 01:04:46,840
Speaker 1: the coronavirus is that it's actually mutating slower than some

1010
01:04:46,960 --> 01:04:49,000
Speaker 1: of the other viruses that we know about, and that

1011
01:04:49,160 --> 01:04:52,360
Speaker 1: is because it has this handy dandy thing called a

1012
01:04:52,400 --> 01:04:57,080
Speaker 1: proof reading enzyme, which does exactly that. It's able to

1013
01:04:57,600 --> 01:05:02,520
Speaker 1: read itself and check for errors and mutations are errors.

1014
01:05:02,600 --> 01:05:07,400
Speaker 1: So if you know, COVID has built in spell check system, right,

1015
01:05:07,920 --> 01:05:11,520
Speaker 1: So it's it's mutating slower than other viruses than we

1016
01:05:11,560 --> 01:05:13,880
Speaker 1: know about, But that doesn't mean it's not mutating. It has,

1017
01:05:14,000 --> 01:05:18,000
Speaker 1: it has mutated. Um, there are studies showing that there

1018
01:05:18,000 --> 01:05:22,080
Speaker 1: are mutated versions of the coronavirus that have sprung up

1019
01:05:22,120 --> 01:05:25,000
Speaker 1: in parts of Europe and and perhaps in other parts

1020
01:05:25,040 --> 01:05:29,120
Speaker 1: of the world. Mutations also will only become an issue

1021
01:05:29,200 --> 01:05:32,600
Speaker 1: when it mutates in a way that makes it easier

1022
01:05:32,640 --> 01:05:38,120
Speaker 1: for the virus to infect people. Okay, So I'm gonna

1023
01:05:38,160 --> 01:05:40,280
Speaker 1: ask a couple of questions about that to make sure

1024
01:05:40,320 --> 01:05:43,640
Speaker 1: that I'm understanding you correctly. So it sounds to me

1025
01:05:43,720 --> 01:05:46,560
Speaker 1: like what you're saying is in the grand scheme of

1026
01:05:46,720 --> 01:05:50,600
Speaker 1: viruses and the way that they mutate. The coronavirus is

1027
01:05:50,680 --> 01:05:54,520
Speaker 1: not mutating in a way that should make us feel nervous.

1028
01:05:54,560 --> 01:05:57,520
Speaker 1: It's quite slow because, as you said, the virus is

1029
01:05:57,560 --> 01:06:01,200
Speaker 1: so smart, it essentially spell checks itself for errors, which

1030
01:06:01,720 --> 01:06:05,360
Speaker 1: scientifically is a good thing because that means we're more

1031
01:06:05,400 --> 01:06:08,840
Speaker 1: likely to, once we have a vaccine, have it be

1032
01:06:09,480 --> 01:06:12,920
Speaker 1: stable and be able to treat the coronavirus because the

1033
01:06:13,000 --> 01:06:17,560
Speaker 1: virus is not changing very rapidly, which many other viruses have,

1034
01:06:18,000 --> 01:06:22,520
Speaker 1: right exactly. Okay, So I think for someone like me,

1035
01:06:22,680 --> 01:06:26,240
Speaker 1: who is certainly interested in science but undoubtedly a layman

1036
01:06:26,280 --> 01:06:29,280
Speaker 1: and not an expert, it's nice to know when when

1037
01:06:29,280 --> 01:06:32,120
Speaker 1: you hear things like viral mutation, I don't know. I

1038
01:06:32,160 --> 01:06:37,040
Speaker 1: think about the AIDS crisis or a movie like Contagion,

1039
01:06:37,160 --> 01:06:38,720
Speaker 1: and I think, oh my god, it's going to be

1040
01:06:38,760 --> 01:06:40,680
Speaker 1: this runaway train and we're never going to get control

1041
01:06:40,760 --> 01:06:43,920
Speaker 1: of it. But that's not what's happening here. That's not

1042
01:06:43,960 --> 01:06:49,920
Speaker 1: what's happening. No, we can we can relax. Um Our scientists,

1043
01:06:49,920 --> 01:06:53,560
Speaker 1: our researchers are working at a pace that is very fast.

1044
01:06:53,720 --> 01:06:56,760
Speaker 1: This is a very urgent situation and the virus is

1045
01:06:56,800 --> 01:07:00,120
Speaker 1: not mutinating in a way that should alarm anybody. We

1046
01:07:00,200 --> 01:07:03,200
Speaker 1: are consistently running an analysis on the sequence of the

1047
01:07:03,320 --> 01:07:05,640
Speaker 1: virus so that we can make sure that it's not

1048
01:07:05,760 --> 01:07:09,040
Speaker 1: mutating in a way that presents a danger to society.

1049
01:07:09,640 --> 01:07:12,640
Speaker 1: And yeah, it's I don't think that people should worry

1050
01:07:12,680 --> 01:07:15,160
Speaker 1: about that at this point in time. We can keep

1051
01:07:15,160 --> 01:07:17,960
Speaker 1: our worry focused on the virus itself and make sure

1052
01:07:17,960 --> 01:07:21,640
Speaker 1: we're implementing precautions. But we don't need to be worried

1053
01:07:21,680 --> 01:07:25,760
Speaker 1: that it's suddenly becoming even worse Thanatority is right, And

1054
01:07:25,800 --> 01:07:31,520
Speaker 1: it's also this also speaks to how important physical distancing is.

1055
01:07:32,360 --> 01:07:34,320
Speaker 1: As long as we can slow the spread of this

1056
01:07:34,480 --> 01:07:39,160
Speaker 1: virus and keep the numbers down, there's lesser of a

1057
01:07:39,240 --> 01:07:42,520
Speaker 1: chance of it becoming a big issue, um if it

1058
01:07:42,560 --> 01:07:45,040
Speaker 1: does mutate, because there's not going to be enough of

1059
01:07:45,080 --> 01:07:49,120
Speaker 1: the virus around. So um yeah, I mean we we

1060
01:07:49,200 --> 01:07:55,720
Speaker 1: just have to keep our precautions in action of physical distancing, handwashing,

1061
01:07:55,880 --> 01:08:00,240
Speaker 1: wearing our masks so we can really kill this. It's

1062
01:08:00,240 --> 01:08:03,560
Speaker 1: a lot of information to process, and it's interesting because

1063
01:08:04,640 --> 01:08:07,720
Speaker 1: in in one sense, you're saying we can feel okay

1064
01:08:07,760 --> 01:08:11,200
Speaker 1: about the virus not mutating not getting worse. But on

1065
01:08:11,240 --> 01:08:13,800
Speaker 1: the other hand, we've been at this, as you mentioned,

1066
01:08:13,800 --> 01:08:17,040
Speaker 1: with the physical distancing and the precautions and the mask

1067
01:08:17,080 --> 01:08:20,200
Speaker 1: wearing and the handwashing and the avoiding large crowds for

1068
01:08:20,280 --> 01:08:22,719
Speaker 1: so many months now that I do think some folks

1069
01:08:22,760 --> 01:08:26,000
Speaker 1: are getting a little bit fatigued in terms of that

1070
01:08:26,120 --> 01:08:31,000
Speaker 1: kind of vigilant fear. But what you're not saying is

1071
01:08:31,040 --> 01:08:38,040
Speaker 1: that the virus is becoming less dangerous, right right, So

1072
01:08:38,040 --> 01:08:46,160
Speaker 1: so um hm, if we think about mutations, right, mutations

1073
01:08:46,240 --> 01:08:51,519
Speaker 1: are pretty much the source of evolution on the grand scale.

1074
01:08:51,960 --> 01:08:55,000
Speaker 1: So if you think about how far humans have come,

1075
01:08:55,479 --> 01:08:58,960
Speaker 1: and you know how far we've developed from early humans

1076
01:08:58,960 --> 01:09:02,360
Speaker 1: to now, we look a loout different than we used to. Um,

1077
01:09:02,439 --> 01:09:06,400
Speaker 1: we are smarter, we have, we just we had a

1078
01:09:06,400 --> 01:09:09,600
Speaker 1: lot going on. Okay, Homo savings, we're doing great, But

1079
01:09:09,640 --> 01:09:12,040
Speaker 1: we weren't always that way. It was a lot of

1080
01:09:12,120 --> 01:09:16,000
Speaker 1: mutations over thousands of years that helped us get to

1081
01:09:16,040 --> 01:09:19,000
Speaker 1: where we are now. Now, if you condense that time

1082
01:09:19,040 --> 01:09:24,960
Speaker 1: frame to a virus, a viral life cycle, um, they don't.

1083
01:09:25,000 --> 01:09:28,720
Speaker 1: They don't live for as long as humans do. They

1084
01:09:29,280 --> 01:09:33,960
Speaker 1: they have generations that are you know, very short. So

1085
01:09:34,040 --> 01:09:38,280
Speaker 1: when you think about evolution on a small scale and

1086
01:09:38,479 --> 01:09:42,000
Speaker 1: how mutations and viruses can play out in a shorter

1087
01:09:42,040 --> 01:09:45,799
Speaker 1: amount of time if there are more opportunities for virus

1088
01:09:45,880 --> 01:09:49,400
Speaker 1: to continue living. Um. So, let's say if we don't

1089
01:09:49,920 --> 01:09:55,360
Speaker 1: physically distance and we continue passing the virus around, and

1090
01:09:55,400 --> 01:09:59,799
Speaker 1: that virus has more opportunities to replicate and continue living

1091
01:10:00,280 --> 01:10:04,400
Speaker 1: and have more cycles, more opportunities for mutations, that's where

1092
01:10:04,400 --> 01:10:08,759
Speaker 1: the danger is because we're giving it more opportunities to change,

1093
01:10:08,760 --> 01:10:12,679
Speaker 1: to be different, to evolve. Um. So, that's why social

1094
01:10:12,720 --> 01:10:14,920
Speaker 1: distancing is so important. We don't want to give the

1095
01:10:15,000 --> 01:10:20,160
Speaker 1: virus more opportunities to become different, right, We want to

1096
01:10:20,200 --> 01:10:22,960
Speaker 1: make sure that we keep the numbers down, and we

1097
01:10:23,000 --> 01:10:25,800
Speaker 1: also want to get a vaccine out so that we

1098
01:10:25,840 --> 01:10:30,400
Speaker 1: can lower these chances for transmission. Can you use your

1099
01:10:30,479 --> 01:10:35,080
Speaker 1: really brilliant way of explaining why masks work. When we

1100
01:10:35,120 --> 01:10:38,840
Speaker 1: talk about our COVID protocols being responsible, how we need

1101
01:10:38,880 --> 01:10:42,280
Speaker 1: to stick to them, lots of people I want to

1102
01:10:42,320 --> 01:10:45,240
Speaker 1: know how long we need to keep wearing masks, and

1103
01:10:45,320 --> 01:10:48,640
Speaker 1: I think reminding us why they work so well is

1104
01:10:48,760 --> 01:10:51,280
Speaker 1: a helpful way to encourage all of us to keep

1105
01:10:51,320 --> 01:10:56,000
Speaker 1: it up well. All masks are not created equal, but

1106
01:10:56,080 --> 01:10:58,240
Speaker 1: what we do know is that having some sort of

1107
01:10:58,280 --> 01:11:03,679
Speaker 1: facial covering is definitely better than nothing, and that there

1108
01:11:03,720 --> 01:11:07,600
Speaker 1: are many studies done that have shown the efficiency of

1109
01:11:07,680 --> 01:11:12,800
Speaker 1: certain masks um and preventing the spread of viral particles.

1110
01:11:12,840 --> 01:11:18,000
Speaker 1: We know that the virus is spread by honestly, any

1111
01:11:18,120 --> 01:11:21,400
Speaker 1: really just any type of air coming out of your mouth. Okay,

1112
01:11:21,400 --> 01:11:28,240
Speaker 1: it could be shouting, sneezing, singing, um talking, sneezing, coughing.

1113
01:11:28,400 --> 01:11:32,559
Speaker 1: All of those actions can spread the virus. So it's

1114
01:11:32,760 --> 01:11:37,160
Speaker 1: best that you not only stay far away from other people,

1115
01:11:37,360 --> 01:11:40,880
Speaker 1: but you also cover your mouth with a mask, and

1116
01:11:41,040 --> 01:11:46,720
Speaker 1: your dog your mouth and your nose. Yes, So what

1117
01:11:46,840 --> 01:11:51,719
Speaker 1: can we do to stay healthy today? What do you recommend? Well?

1118
01:11:51,760 --> 01:11:54,240
Speaker 1: I think that taking care of your mental health is

1119
01:11:54,400 --> 01:11:56,880
Speaker 1: very important. We talk a lot about physical health, but

1120
01:11:56,960 --> 01:12:00,160
Speaker 1: the reality for many people is is different. We're all

1121
01:12:00,200 --> 01:12:02,360
Speaker 1: going through a lot of different things, but most of

1122
01:12:02,439 --> 01:12:06,040
Speaker 1: us have not had much social contact with other people. Um.

1123
01:12:06,120 --> 01:12:09,439
Speaker 1: I implore that people do find a way to stay

1124
01:12:09,439 --> 01:12:14,840
Speaker 1: connected with other humans because human contact, social socialization is

1125
01:12:15,439 --> 01:12:18,720
Speaker 1: a natural human tendency. We are a social species, So

1126
01:12:18,800 --> 01:12:22,720
Speaker 1: make sure you're still talking to people, still expressing yourself

1127
01:12:23,040 --> 01:12:26,200
Speaker 1: and engaging with others, even if it's just walking on

1128
01:12:26,280 --> 01:12:29,320
Speaker 1: your street and smiling at a neighbor. Little things like

1129
01:12:29,400 --> 01:12:32,040
Speaker 1: that can really make a great impact on your day.

1130
01:12:32,520 --> 01:12:38,040
Speaker 1: UM again, continue practicing your COVID nineteen precautions. Find someone

1131
01:12:38,520 --> 01:12:42,080
Speaker 1: in science that you trust to learn information from and

1132
01:12:42,160 --> 01:12:44,519
Speaker 1: kind of stick with them. Make them be your science buddy.

1133
01:12:44,880 --> 01:12:47,960
Speaker 1: Follow them on social media if you can, UM find

1134
01:12:47,960 --> 01:12:51,839
Speaker 1: a trusted news source. An official news source like government

1135
01:12:51,840 --> 01:12:55,040
Speaker 1: agencies can also be a great source of information and

1136
01:12:55,040 --> 01:12:58,479
Speaker 1: should should honestly be your first source of information. And

1137
01:12:59,360 --> 01:13:01,719
Speaker 1: you know, don't don't give up, hope. We are all

1138
01:13:01,760 --> 01:13:05,559
Speaker 1: in this together and we just need to stay strong.

1139
01:13:05,760 --> 01:13:08,960
Speaker 1: We can get through this. Raven. It's so fun to

1140
01:13:09,640 --> 01:13:11,720
Speaker 1: hang with you. Even though it's over zoom, I wish

1141
01:13:11,760 --> 01:13:15,360
Speaker 1: it was in person. You are the most effusive and

1142
01:13:15,360 --> 01:13:18,800
Speaker 1: and just fun human to be around. And it's no

1143
01:13:18,880 --> 01:13:21,360
Speaker 1: surprise to me why all of your students adore you

1144
01:13:21,439 --> 01:13:24,760
Speaker 1: so much and why your science content is going so

1145
01:13:24,920 --> 01:13:29,160
Speaker 1: viral online. UM listeners at home will be linking to

1146
01:13:29,240 --> 01:13:33,080
Speaker 1: everything in this week's episode, will post videos and let

1147
01:13:33,080 --> 01:13:35,360
Speaker 1: you know where you can find all of Raven's content.

1148
01:13:36,800 --> 01:13:39,960
Speaker 1: My last question for you. I don't want to let

1149
01:13:39,960 --> 01:13:42,200
Speaker 1: you go, but you have things to do, like saving

1150
01:13:42,200 --> 01:13:45,200
Speaker 1: the planet from COVID, so I'm going to let you go.

1151
01:13:45,680 --> 01:13:48,080
Speaker 1: But the last question I love to ask everybody who

1152
01:13:48,080 --> 01:13:51,759
Speaker 1: comes on the show is, as the podcast is titled

1153
01:13:52,040 --> 01:13:55,280
Speaker 1: work in Progress, and we all think about where we

1154
01:13:55,320 --> 01:13:58,040
Speaker 1: are in this moment, what feels like a work in

1155
01:13:58,080 --> 01:14:00,920
Speaker 1: progress in your life right now? Um? So is this

1156
01:14:00,960 --> 01:14:03,600
Speaker 1: a work in progress, like a little literal work in

1157
01:14:03,680 --> 01:14:08,320
Speaker 1: progress or any anything. It could be personal or professional,

1158
01:14:08,560 --> 01:14:11,360
Speaker 1: or something you're thinking about for the world, really whatever

1159
01:14:11,439 --> 01:14:15,720
Speaker 1: comes to mind. Gosh, there's oh my gosh, this is

1160
01:14:15,720 --> 01:14:21,479
Speaker 1: not all day. I'm always on the go. I'm I'm

1161
01:14:21,520 --> 01:14:24,360
Speaker 1: a problem solver, so whenever I see a problem, I

1162
01:14:24,400 --> 01:14:27,160
Speaker 1: try to solve it. And there's there are a couple

1163
01:14:27,200 --> 01:14:32,120
Speaker 1: of things that I'm working on. One of them is, uh,

1164
01:14:32,240 --> 01:14:36,240
Speaker 1: I'm working on producing a few shows in the science

1165
01:14:37,000 --> 01:14:41,040
Speaker 1: and one of them is called Nerdy Jobs, and it

1166
01:14:41,160 --> 01:14:45,280
Speaker 1: is essentially me going around in the world shadowing different

1167
01:14:45,520 --> 01:14:50,320
Speaker 1: scientists and other STEM professionals and working with them to

1168
01:14:50,360 --> 01:14:53,200
Speaker 1: see what kind of cool things are doing, and also

1169
01:14:53,439 --> 01:14:59,080
Speaker 1: to showcase that STEM is fun and so cool cool,

1170
01:14:59,120 --> 01:15:03,479
Speaker 1: and that like we can we can cry, laugh, uh, scream, hollar,

1171
01:15:03,760 --> 01:15:06,960
Speaker 1: but we're learning through it all and we're having fun.

1172
01:15:07,360 --> 01:15:11,280
Speaker 1: So that's one thing that I'm excited about. And then, um,

1173
01:15:11,400 --> 01:15:15,200
Speaker 1: the other thing that I I'm working on is finding

1174
01:15:15,200 --> 01:15:19,920
Speaker 1: out a way to create more opportunities for scientists who

1175
01:15:19,960 --> 01:15:24,960
Speaker 1: are pursuing like higher degrees, their terminal degrees, scientists who

1176
01:15:25,000 --> 01:15:30,759
Speaker 1: are producing PhDs, to have opportunities to also pursue public

1177
01:15:30,760 --> 01:15:36,479
Speaker 1: outreach and education. There is a huge issue in science

1178
01:15:36,520 --> 01:15:41,040
Speaker 1: culture where when you get to the PhD level, you're

1179
01:15:41,080 --> 01:15:44,479
Speaker 1: not I mean, it depends on where you are, but

1180
01:15:44,520 --> 01:15:49,040
Speaker 1: if you're at a research institution, you are often encouraged

1181
01:15:49,080 --> 01:15:53,240
Speaker 1: to not be excited about teaching, and you're often encouraged

1182
01:15:53,280 --> 01:15:57,400
Speaker 1: to solely focus on your research. And it's often discouraged

1183
01:15:57,960 --> 01:16:01,439
Speaker 1: to have a desire to want to teach, to want

1184
01:16:01,439 --> 01:16:04,519
Speaker 1: to engage with the public, and to to take your

1185
01:16:04,520 --> 01:16:07,599
Speaker 1: focus away from strictly research and add some humanity and

1186
01:16:07,640 --> 01:16:12,640
Speaker 1: compassion and uh community building into your life. And that

1187
01:16:12,840 --> 01:16:16,080
Speaker 1: is a huge problem. I recently had a discussion online

1188
01:16:16,120 --> 01:16:20,200
Speaker 1: about this on my own platform, and many other scientists

1189
01:16:20,280 --> 01:16:23,240
Speaker 1: agree that this is what's missing in our culture and

1190
01:16:23,280 --> 01:16:26,920
Speaker 1: it's contributing to a major problem in greater society. As

1191
01:16:26,920 --> 01:16:30,439
Speaker 1: we've talked about earlier today, a lot of the problems

1192
01:16:30,439 --> 01:16:33,640
Speaker 1: that we're experiencing right now, this pandemic is because of

1193
01:16:33,680 --> 01:16:37,720
Speaker 1: the public disconnect with science, and a part of that issue, um,

1194
01:16:37,880 --> 01:16:40,479
Speaker 1: is due to the fact that science itself is not

1195
01:16:40,600 --> 01:16:44,640
Speaker 1: letting scientists connect with the public and like have have

1196
01:16:44,760 --> 01:16:47,960
Speaker 1: that connection. So, UM, you know, that's something that I

1197
01:16:48,080 --> 01:16:52,360
Speaker 1: really want to work on. I'm I don't know where

1198
01:16:52,360 --> 01:16:55,320
Speaker 1: I'm going to start, but that's absolutely on my agenda

1199
01:16:55,400 --> 01:16:58,360
Speaker 1: to create more opportunities for scientists who who love the

1200
01:16:58,400 --> 01:17:02,880
Speaker 1: public like I do. That's so cool, And I can say,

1201
01:17:02,920 --> 01:17:04,840
Speaker 1: as a member of the public who love science that

1202
01:17:05,479 --> 01:17:09,720
Speaker 1: I would be so excited to have more access to

1203
01:17:09,880 --> 01:17:14,559
Speaker 1: conversations like this and and to watch you guys speak

1204
01:17:14,560 --> 01:17:16,800
Speaker 1: in real time about the work that you're doing and

1205
01:17:17,439 --> 01:17:19,559
Speaker 1: how it's done. I think you're right. I think it

1206
01:17:19,560 --> 01:17:23,799
Speaker 1: would really welcome so many people into the scientific arena

1207
01:17:23,960 --> 01:17:27,439
Speaker 1: and and make them feel like they didn't have to

1208
01:17:27,479 --> 01:17:31,320
Speaker 1: be nervous or afraid to be there. So, UM, tell

1209
01:17:31,320 --> 01:17:34,880
Speaker 1: me where to sign up. Well, it's study that you

1210
01:17:34,960 --> 01:17:37,200
Speaker 1: asked that, because I actually do have a talk show.

1211
01:17:39,160 --> 01:17:44,760
Speaker 1: It's called Stembassy and it is my science advocacy organization.

1212
01:17:45,000 --> 01:17:48,640
Speaker 1: I run it with four other women's scientists and we

1213
01:17:48,680 --> 01:17:51,479
Speaker 1: have a weekly live show actually where we all gather

1214
01:17:51,560 --> 01:17:55,120
Speaker 1: and we invite scientists to come on and talk about

1215
01:17:55,160 --> 01:17:59,679
Speaker 1: their research. We play games, we have fun, we make jokes. Um.

1216
01:17:59,720 --> 01:18:03,400
Speaker 1: But overall it's it's been a really wonderful opportunity for

1217
01:18:03,439 --> 01:18:07,920
Speaker 1: the public to come and engage with scientists in real time. UM.

1218
01:18:07,960 --> 01:18:10,640
Speaker 1: And so yeah that that exists. I made it, and

1219
01:18:11,520 --> 01:18:14,080
Speaker 1: you're the joint. I'd love to have you one. I'll

1220
01:18:14,120 --> 01:18:18,840
Speaker 1: be there, let's plan it. Yes, Yes, awesome. Thank you

1221
01:18:18,880 --> 01:18:21,880
Speaker 1: so much for today. This has been so fun. Likewise,

1222
01:18:22,040 --> 01:18:24,160
Speaker 1: I had so much fun. Thank you for having me.

1223
01:18:28,040 --> 01:18:31,200
Speaker 1: This show is executive produced by me, Sophia Bush, and

1224
01:18:31,280 --> 01:18:35,759
Speaker 1: sim Sarna. Our associate producer is Cate Linley. Our editor

1225
01:18:35,880 --> 01:18:38,760
Speaker 1: is Josh Wendish, and our music was written by Jack

1226
01:18:38,760 --> 01:18:41,960
Speaker 1: Garrett and produced by Mark Foster. This show is brought

1227
01:18:42,000 --> 01:18:46,120
Speaker 1: to you by Brilliant Anatomy