1 00:00:00,320 --> 00:00:02,120 Speaker 1: There Are No Girls on the Internet is doing a 2 00:00:02,160 --> 00:00:05,840 Speaker 1: live show at Caveat in New York City on Friday. 3 00:00:06,040 --> 00:00:08,239 Speaker 1: You can also attend virtually from wherever you're at in 4 00:00:08,280 --> 00:00:11,479 Speaker 1: the world. We'll have super cool guests, a meet and greet, 5 00:00:11,520 --> 00:00:14,240 Speaker 1: and a lot more. Go to Tango dot com slash 6 00:00:14,320 --> 00:00:16,919 Speaker 1: Live to get tickets. That's t A n g O 7 00:00:17,120 --> 00:00:20,360 Speaker 1: t I dot com slash Live and I cannot wait 8 00:00:20,360 --> 00:00:23,800 Speaker 1: to see you there. Dannis fully served as their role model, 9 00:00:24,280 --> 00:00:28,200 Speaker 1: and when we asked them why, it was because she 10 00:00:28,480 --> 00:00:32,960 Speaker 1: was uh in control of her destiny. She didn't have 11 00:00:33,120 --> 00:00:45,160 Speaker 1: to be saved. There Are No Girls on the Internet 12 00:00:45,200 --> 00:00:47,680 Speaker 1: as a production of I Heart Radio and Unbossed Creative. 13 00:00:52,040 --> 00:00:54,160 Speaker 1: I'm Bridget Todd and this is There Are No Girls 14 00:00:54,160 --> 00:00:59,240 Speaker 1: on the Internet. So I was obsessed with the TV 15 00:00:59,320 --> 00:01:03,440 Speaker 1: show The X Files growing up, specifically because one doctor 16 00:01:03,520 --> 00:01:07,200 Speaker 1: Dana Scully. Now, I know that woman scientist is kind 17 00:01:07,200 --> 00:01:09,640 Speaker 1: of a television genre now with shows like Phones and 18 00:01:09,720 --> 00:01:12,480 Speaker 1: c Side, but back when I was growing up, Dana 19 00:01:12,560 --> 00:01:15,480 Speaker 1: Scully was one of the only successful, capable women in 20 00:01:15,560 --> 00:01:19,479 Speaker 1: stem on television that I had Before Scully, a scientist 21 00:01:19,480 --> 00:01:22,120 Speaker 1: on TV was usually a loner, white dude in a 22 00:01:22,200 --> 00:01:26,319 Speaker 1: lab code, and this critical lack of representation kept women 23 00:01:26,319 --> 00:01:28,720 Speaker 1: and girls like me from being able to see ourselves 24 00:01:28,800 --> 00:01:33,240 Speaker 1: meaningly reflected in STEM fields. According to the Gina Davis 25 00:01:33,240 --> 00:01:37,840 Speaker 1: Institute on Gender and Media YEP, spearheaded by that Gina Davis, 26 00:01:37,840 --> 00:01:41,240 Speaker 1: this portrayal reinforces the belief that science is a male pursuit, 27 00:01:41,680 --> 00:01:44,280 Speaker 1: one that is held by many children, adolescents, and adults. 28 00:01:44,760 --> 00:01:47,960 Speaker 1: Children start implicitly pairing men and math as early as 29 00:01:48,000 --> 00:01:52,080 Speaker 1: age seven, a bias that continues into adulthood. The Davis 30 00:01:52,080 --> 00:01:56,000 Speaker 1: Institute researches gender representation and media and advocates for the 31 00:01:56,040 --> 00:01:59,520 Speaker 1: equal representation of women. Their researchers looked at the impact 32 00:01:59,560 --> 00:02:02,120 Speaker 1: that Dana Scully had on women and girls in STEM 33 00:02:02,160 --> 00:02:05,640 Speaker 1: and the importance of representation in media. I have the 34 00:02:05,680 --> 00:02:10,400 Speaker 1: privilege of serving as the President and CEO of the 35 00:02:10,480 --> 00:02:13,600 Speaker 1: Gina Davis Institute on Gender and Media. So how did 36 00:02:13,600 --> 00:02:17,799 Speaker 1: an actor like Gina Davis, the woman behind iconic performances 37 00:02:17,840 --> 00:02:19,760 Speaker 1: in films Like a League of their Own, come to 38 00:02:19,800 --> 00:02:23,120 Speaker 1: start of institute dedicated to equity and media. Well, it 39 00:02:23,240 --> 00:02:26,560 Speaker 1: actually came out of her experience as being a mom. 40 00:02:26,600 --> 00:02:31,120 Speaker 1: Like any mom, she was watching content with her actual 41 00:02:31,240 --> 00:02:34,200 Speaker 1: daughter at the time, it was a toddler, and she 42 00:02:34,320 --> 00:02:40,440 Speaker 1: was struck by the disparity and lack of female presence, 43 00:02:41,440 --> 00:02:46,120 Speaker 1: fictional presence in the content that she was showing her daughter. 44 00:02:46,280 --> 00:02:49,080 Speaker 1: And like any mom, you turned to your friends, You're like, hey, 45 00:02:49,440 --> 00:02:51,800 Speaker 1: did you notice in such and such show or movie 46 00:02:51,800 --> 00:02:54,680 Speaker 1: there just wasn't a lot of female characters? And her 47 00:02:54,680 --> 00:02:57,040 Speaker 1: friends would say, no, we didn't really notice that. And 48 00:02:57,080 --> 00:03:00,600 Speaker 1: then she asked the same question when she meet with 49 00:03:02,000 --> 00:03:08,400 Speaker 1: producers or other creators for potential acting work, and they 50 00:03:08,400 --> 00:03:11,120 Speaker 1: would say, no, no, we had this character, we had 51 00:03:11,160 --> 00:03:16,040 Speaker 1: that character, and they were naming wonderful female characters, but 52 00:03:16,120 --> 00:03:20,040 Speaker 1: they were it was one. And she thought, my goodness, 53 00:03:20,080 --> 00:03:25,600 Speaker 1: in the twenty one century, women and girls are fifty 54 00:03:25,800 --> 00:03:29,200 Speaker 1: something per cent of the population and they're not being 55 00:03:29,240 --> 00:03:35,160 Speaker 1: reflected in popular content. And the concern came because of 56 00:03:35,200 --> 00:03:40,120 Speaker 1: the messaging. What's the message that not only my daughter, 57 00:03:40,320 --> 00:03:44,080 Speaker 1: but my two young sons are getting about where women 58 00:03:44,080 --> 00:03:47,200 Speaker 1: and girls fit and their importance. Are we saying that 59 00:03:47,240 --> 00:03:50,360 Speaker 1: they don't share the sandbox? Are we saying that they're 60 00:03:50,400 --> 00:03:57,240 Speaker 1: not as important? And that's what gave her the uh, 61 00:03:57,360 --> 00:04:00,760 Speaker 1: the drive to say, I need to at the research. 62 00:04:00,880 --> 00:04:04,320 Speaker 1: I want to know if I'm correct, you know, am 63 00:04:04,360 --> 00:04:09,920 Speaker 1: I truly seeing this gap um and this disparity. And 64 00:04:10,240 --> 00:04:14,160 Speaker 1: so if you hear Gina talk, she'll say, I didn't 65 00:04:14,240 --> 00:04:21,080 Speaker 1: mean to start a whole institute. Once she got started, Uh, 66 00:04:21,279 --> 00:04:24,680 Speaker 1: she tends to become very laser focused, like with her 67 00:04:24,800 --> 00:04:29,080 Speaker 1: archery and many other things that she does. And that's 68 00:04:29,080 --> 00:04:33,360 Speaker 1: how the institute came about. So it came about, you know, 69 00:04:33,400 --> 00:04:37,280 Speaker 1: from her being a mom and concerned about you know, 70 00:04:37,680 --> 00:04:40,200 Speaker 1: what kind of biases her daughter going to grow up with, 71 00:04:40,240 --> 00:04:44,520 Speaker 1: you know, her sons, etcetera. Uh, And that's that's that's 72 00:04:44,520 --> 00:04:47,560 Speaker 1: how it got started. Yeah, I'm so glad that you 73 00:04:47,640 --> 00:04:51,320 Speaker 1: talked about how it's important not just for young women 74 00:04:51,320 --> 00:04:54,240 Speaker 1: and girls, but also for boys, for people of any 75 00:04:54,360 --> 00:04:59,080 Speaker 1: gender to understand that you know, yes, women and girls 76 00:04:59,160 --> 00:05:03,040 Speaker 1: are are self actual, self actualized. We have you know, 77 00:05:03,480 --> 00:05:06,320 Speaker 1: roles in society and we're in the workplace. Like it's 78 00:05:06,320 --> 00:05:09,080 Speaker 1: not just important for women and girls, it is important 79 00:05:09,080 --> 00:05:11,719 Speaker 1: for us, but also for everybody to see the ways 80 00:05:11,760 --> 00:05:14,359 Speaker 1: that women and girls should be integrated in society and 81 00:05:14,360 --> 00:05:17,719 Speaker 1: see a diverse you know, array of what that actually 82 00:05:17,760 --> 00:05:21,760 Speaker 1: looks like. Yeah. So for example, if you grow up 83 00:05:22,839 --> 00:05:28,760 Speaker 1: seeing say, a media landscape that is full of many 84 00:05:28,800 --> 00:05:32,960 Speaker 1: diverse people. I'm not just saying boys, girls, and give 85 00:05:33,080 --> 00:05:35,360 Speaker 1: any type people with disabilities at p t q A. 86 00:05:35,880 --> 00:05:40,760 Speaker 1: If that's normalized in what you see in the fictional world, 87 00:05:41,240 --> 00:05:46,359 Speaker 1: then it transfers into your real life. When you're at school, 88 00:05:46,839 --> 00:05:50,760 Speaker 1: maybe when you're playing sports, maybe your first job. You 89 00:05:50,800 --> 00:05:54,599 Speaker 1: would expect to see that flavor, those colors of the 90 00:05:54,680 --> 00:05:58,440 Speaker 1: rainbows reflected in an environment, because that's all you've ever seen, 91 00:05:58,560 --> 00:06:02,080 Speaker 1: that's what you've known. Now we know that's not exactly 92 00:06:02,120 --> 00:06:07,480 Speaker 1: the real world, but it normalizes it. Media is playing 93 00:06:07,720 --> 00:06:12,839 Speaker 1: as important of a role in influencing their societal beliefs 94 00:06:12,880 --> 00:06:17,520 Speaker 1: as much as church, sports, sleeping, you know, all of 95 00:06:17,520 --> 00:06:21,320 Speaker 1: those other activities. So you can't ignore it and say, oh, 96 00:06:21,400 --> 00:06:24,760 Speaker 1: it's just to make believe. No, no, no no, Now, what 97 00:06:25,720 --> 00:06:29,640 Speaker 1: plays out you know on screen can also transfer to 98 00:06:29,720 --> 00:06:32,359 Speaker 1: real life and could be in a wonderful way, in 99 00:06:32,360 --> 00:06:36,719 Speaker 1: a really positive way. Now, representation is not the end all, 100 00:06:36,760 --> 00:06:39,440 Speaker 1: be all, but we do know that it matters because 101 00:06:39,480 --> 00:06:41,880 Speaker 1: everyone deserves to see themselves reflected in the stories that 102 00:06:41,920 --> 00:06:45,400 Speaker 1: we consume. And to that end, the Davis Institute uses 103 00:06:45,440 --> 00:06:48,039 Speaker 1: technology to put some data behind who gets treated like 104 00:06:48,080 --> 00:06:51,200 Speaker 1: a full main character and who has to be satisfied 105 00:06:51,240 --> 00:06:54,120 Speaker 1: with just seeing themselves reflected as a side character and 106 00:06:54,200 --> 00:06:59,000 Speaker 1: someone else's story. One of my on screen idols, Miss 107 00:06:59,080 --> 00:07:01,360 Speaker 1: Dana Scully, put out of your organization to put out 108 00:07:01,360 --> 00:07:04,240 Speaker 1: a report about the Skully Effect, which I definitely want 109 00:07:04,240 --> 00:07:06,400 Speaker 1: to talk about, But I have to ask you. Know 110 00:07:06,480 --> 00:07:10,280 Speaker 1: you mentioned how you know a diverse array of folks 111 00:07:10,320 --> 00:07:12,560 Speaker 1: can have a diverse array of different kinds of jobs, 112 00:07:12,920 --> 00:07:16,200 Speaker 1: you know, applying that to you personally, you have had 113 00:07:16,280 --> 00:07:20,800 Speaker 1: this incredible career that has spanned over so many fields philanthropy. 114 00:07:20,840 --> 00:07:24,000 Speaker 1: You're an Emmy nominated enterhayment professional. You used to run 115 00:07:24,000 --> 00:07:25,560 Speaker 1: the Hallmark Channel, which I have to tell you is 116 00:07:25,560 --> 00:07:29,040 Speaker 1: where I get the bulk of my holiday programming, love 117 00:07:29,080 --> 00:07:32,160 Speaker 1: the holiday movies. How did you personally get plugged into 118 00:07:32,160 --> 00:07:37,040 Speaker 1: this work at the institute? I was on a journey, uh, 119 00:07:37,240 --> 00:07:40,280 Speaker 1: like I think we all are. And I'm the first 120 00:07:40,320 --> 00:07:44,080 Speaker 1: person to go to college in my media family. I 121 00:07:44,240 --> 00:07:49,600 Speaker 1: come from, you know, generations of of immigrants like many 122 00:07:49,640 --> 00:07:55,200 Speaker 1: other people, and so there wasn't a pathway or a 123 00:07:55,400 --> 00:08:00,960 Speaker 1: pipeline pre provided for me. And so going to college, 124 00:08:01,360 --> 00:08:04,240 Speaker 1: which we could debate the value of that. That that's 125 00:08:04,280 --> 00:08:08,200 Speaker 1: another podcast. You know, at that time, going to college 126 00:08:08,240 --> 00:08:12,680 Speaker 1: was a really big deal and also trying to find 127 00:08:12,680 --> 00:08:16,240 Speaker 1: your way. And this is a secret about me. Now 128 00:08:16,280 --> 00:08:18,760 Speaker 1: you're really going to think I'm very strange, and I'm 129 00:08:18,760 --> 00:08:21,640 Speaker 1: sure are your listeners are. But how do you get 130 00:08:21,680 --> 00:08:25,520 Speaker 1: information about people? And this is like pre you know, 131 00:08:25,560 --> 00:08:30,320 Speaker 1: pre iOS universe. I would read the obituaries, and as 132 00:08:30,400 --> 00:08:32,319 Speaker 1: much as I know, there's been a lot of argument 133 00:08:32,360 --> 00:08:37,280 Speaker 1: about how the obituaries have not favored women, but I 134 00:08:37,320 --> 00:08:43,000 Speaker 1: was constantly looking for unsung heroines. Were there people out 135 00:08:43,040 --> 00:08:47,319 Speaker 1: there doing things that could help guide me or give 136 00:08:47,360 --> 00:08:50,959 Speaker 1: me ideas? And I read about so many great women 137 00:08:51,000 --> 00:08:53,600 Speaker 1: who invented things and people like you just would never 138 00:08:53,679 --> 00:08:55,440 Speaker 1: hear about. Now, of course you would know who they are. 139 00:08:56,160 --> 00:09:01,360 Speaker 1: And I was a executive groupie, so I would just 140 00:09:01,720 --> 00:09:06,440 Speaker 1: latch on and look at great men and women who 141 00:09:06,440 --> 00:09:08,960 Speaker 1: were doing interesting things in their careers. And I would 142 00:09:08,960 --> 00:09:11,800 Speaker 1: look at what was the path that they took. Because 143 00:09:11,840 --> 00:09:17,079 Speaker 1: I didn't have anyone leading me, um, I didn't have anyone, 144 00:09:17,200 --> 00:09:22,640 Speaker 1: you know, mentoring me um. So So with that, how 145 00:09:22,640 --> 00:09:27,520 Speaker 1: do you get experience? And uh, A dear family friend 146 00:09:28,160 --> 00:09:31,360 Speaker 1: love dating very high powered women. God bless him, he's 147 00:09:31,400 --> 00:09:35,760 Speaker 1: still alive, Thank you, Frank. And I wound up doing 148 00:09:35,760 --> 00:09:37,880 Speaker 1: a lot of internships with a lot of his high 149 00:09:38,000 --> 00:09:42,559 Speaker 1: powered girlfriends. That's how I got my gigs um and 150 00:09:42,679 --> 00:09:46,479 Speaker 1: so I started interning from the time I was seventeen 151 00:09:47,000 --> 00:09:49,880 Speaker 1: all the way until I graduated college at twenty one, 152 00:09:49,880 --> 00:09:52,880 Speaker 1: where I wound up landing a full time job at 153 00:09:52,920 --> 00:09:56,480 Speaker 1: at ABC because of all the internships that I've done, 154 00:09:56,559 --> 00:09:59,600 Speaker 1: That's kind of how it started for me. Wow, I'm 155 00:09:59,600 --> 00:10:03,360 Speaker 1: sel lad to hear all the different places your professional 156 00:10:03,440 --> 00:10:05,800 Speaker 1: journey took you, and I'm even more happy that you 157 00:10:05,880 --> 00:10:09,760 Speaker 1: landed somewhere that is really changing. I think our understanding 158 00:10:09,800 --> 00:10:12,160 Speaker 1: of the role that represent that media can play and 159 00:10:12,200 --> 00:10:15,600 Speaker 1: how important representation is for the kind of world that 160 00:10:15,640 --> 00:10:16,840 Speaker 1: we want to live in. You know, some of the 161 00:10:16,880 --> 00:10:20,240 Speaker 1: tools that the David's Institute puts out are so incredible. 162 00:10:20,280 --> 00:10:23,000 Speaker 1: You know, things like the Inclusion Quotient that uses machine 163 00:10:23,080 --> 00:10:26,360 Speaker 1: learning to analyze who speaks the most in different types 164 00:10:26,440 --> 00:10:29,360 Speaker 1: of media, or spell Check for Bias that uses the 165 00:10:29,440 --> 00:10:35,560 Speaker 1: Institute's human expert coding to determine the representation of six identities, gender, race, ethnicity, 166 00:10:35,720 --> 00:10:40,280 Speaker 1: lgbt QUI A plus visibility, body type representation, age representation. 167 00:10:40,679 --> 00:10:43,400 Speaker 1: I guess my question is how are First of all, 168 00:10:43,480 --> 00:10:47,040 Speaker 1: I just want to shout out the depths to which 169 00:10:47,240 --> 00:10:50,840 Speaker 1: the Institute is like really putting doing in this work 170 00:10:50,840 --> 00:10:53,079 Speaker 1: in a granular way. And then also, I guess my 171 00:10:53,240 --> 00:10:56,200 Speaker 1: question is how are all of these tools and studies 172 00:10:56,280 --> 00:11:00,240 Speaker 1: being used to create a more equitable media landscape. Well, 173 00:11:00,840 --> 00:11:04,079 Speaker 1: first of all, I can't take credit at all for 174 00:11:05,400 --> 00:11:10,320 Speaker 1: the depth of wanting to see not only how many 175 00:11:10,679 --> 00:11:16,280 Speaker 1: female characters maybe on screen, but what is their sense 176 00:11:16,360 --> 00:11:19,000 Speaker 1: of agency? That came from Gina, So one of the 177 00:11:19,120 --> 00:11:22,200 Speaker 1: things that she always wanted to know is okay, great, 178 00:11:22,280 --> 00:11:25,080 Speaker 1: I can count on one hand how many female characters are, 179 00:11:25,240 --> 00:11:30,200 Speaker 1: but are they being seen and heard with the same 180 00:11:30,320 --> 00:11:35,079 Speaker 1: weight as their male counterparts? And that data point was 181 00:11:35,280 --> 00:11:40,400 Speaker 1: not humanly possible. And it wasn't until we received a 182 00:11:41,480 --> 00:11:46,199 Speaker 1: lovely technology grant from Google dot org back in and 183 00:11:46,400 --> 00:11:51,480 Speaker 1: we found true partners in USC Perturby School of Engineering 184 00:11:51,559 --> 00:11:56,840 Speaker 1: led by Dr tre Naryan to come together and Jine said, 185 00:11:56,880 --> 00:11:59,839 Speaker 1: I want to know the screen in speaking time, and 186 00:12:00,040 --> 00:12:02,360 Speaker 1: and this is really funny and if you ever have 187 00:12:02,480 --> 00:12:05,760 Speaker 1: a chance to Meetrie and his team of engineers. Gina 188 00:12:05,800 --> 00:12:07,319 Speaker 1: and I sat with him and said, we want to 189 00:12:07,360 --> 00:12:10,240 Speaker 1: think of the jiggy and now you've got the most 190 00:12:10,720 --> 00:12:17,240 Speaker 1: brainiac brilliant scientists engineers. And they looked at us like, what, UM, Yeah, 191 00:12:17,320 --> 00:12:19,920 Speaker 1: we want to thinking. We want the thinking to extract 192 00:12:20,400 --> 00:12:22,920 Speaker 1: screen and they built it right, They did it, UM. 193 00:12:23,760 --> 00:12:27,839 Speaker 1: And what we found is, uh that even if it 194 00:12:28,000 --> 00:12:29,719 Speaker 1: was a top of the call sheet, right, so you 195 00:12:29,760 --> 00:12:32,720 Speaker 1: had a female lead, male lead, that the female characters 196 00:12:32,920 --> 00:12:35,520 Speaker 1: were on screen and speaking a third of the time 197 00:12:35,640 --> 00:12:38,920 Speaker 1: less even though they have the same weight, say, in 198 00:12:39,080 --> 00:12:41,800 Speaker 1: terms of of a call sheet. And that was not 199 00:12:42,080 --> 00:12:44,800 Speaker 1: possible until we had the g d i Q, the 200 00:12:45,000 --> 00:12:49,880 Speaker 1: inclusion quotation, which uses, as you mentioned, machine learning plus 201 00:12:50,920 --> 00:12:54,400 Speaker 1: human expert coders to do the other things. Because as 202 00:12:54,440 --> 00:12:59,599 Speaker 1: you mentioned, we look at intersectionality and for us it 203 00:12:59,720 --> 00:13:05,880 Speaker 1: just ectionality, which Kimberly Crenshaw, Thank you, Kimberly beautifully coined 204 00:13:06,480 --> 00:13:10,199 Speaker 1: at Columbia. But in our world we look at the 205 00:13:10,320 --> 00:13:13,760 Speaker 1: intersection between gender, race, ethnicity, l g P, t q 206 00:13:14,000 --> 00:13:18,240 Speaker 1: I A disabilities, age fifty plus, and body type. So 207 00:13:18,520 --> 00:13:21,920 Speaker 1: our intersectionality is a little bit different than the traditional 208 00:13:22,400 --> 00:13:26,480 Speaker 1: full on definition. UM. But that's you know what we 209 00:13:26,720 --> 00:13:32,160 Speaker 1: we look at. You know, that intersection. The tools that 210 00:13:32,280 --> 00:13:35,679 Speaker 1: the Davis Institute builds and champions opens up the door 211 00:13:35,800 --> 00:13:38,520 Speaker 1: for media makers to make their stories more inclusive and 212 00:13:38,960 --> 00:13:42,679 Speaker 1: better represent a diversity of identities. It's been used as 213 00:13:42,720 --> 00:13:46,079 Speaker 1: an auditing tool for people to measure where are we 214 00:13:47,000 --> 00:13:48,720 Speaker 1: where do we need to go? How are we doing 215 00:13:49,720 --> 00:13:54,199 Speaker 1: It's it would be complicated, uh for us, and it 216 00:13:54,320 --> 00:13:57,400 Speaker 1: can be used in a live production kind of way. 217 00:13:58,400 --> 00:14:01,200 Speaker 1: So it's been used as an auditing to by many 218 00:14:01,600 --> 00:14:08,240 Speaker 1: leading brands and entertainment entities. What we once we socialize that, 219 00:14:08,360 --> 00:14:13,079 Speaker 1: we thought, well, what could really be an intervention? And 220 00:14:13,280 --> 00:14:17,080 Speaker 1: that's where we turned back to our partners that us 221 00:14:17,120 --> 00:14:20,440 Speaker 1: SEE who had had this patented text to IP, and 222 00:14:20,560 --> 00:14:24,160 Speaker 1: we said, we want a different thing, um. And essentially 223 00:14:24,760 --> 00:14:28,080 Speaker 1: the combination of their text tool IP and our human 224 00:14:28,120 --> 00:14:31,840 Speaker 1: expert coders were able to look at words and so 225 00:14:32,040 --> 00:14:34,760 Speaker 1: think about it. You know, you're writing a script for 226 00:14:34,880 --> 00:14:40,000 Speaker 1: something and for the people that are being charged with 227 00:14:40,120 --> 00:14:44,680 Speaker 1: looking at cultural equity in content and then having to 228 00:14:44,840 --> 00:14:47,800 Speaker 1: have a discussion about it with the people who are 229 00:14:47,840 --> 00:14:52,840 Speaker 1: actually producing and making and crafting the content. Uh. This 230 00:14:53,120 --> 00:14:55,600 Speaker 1: is a way for them to come together around a 231 00:14:55,800 --> 00:15:02,640 Speaker 1: data driven conversation versus a theory or opinion. And essentially 232 00:15:03,120 --> 00:15:07,000 Speaker 1: it's very pragmatic in that we look at who is speaking, 233 00:15:07,680 --> 00:15:11,400 Speaker 1: who is contributing dialogue, and it's just who is showing up? 234 00:15:11,600 --> 00:15:14,560 Speaker 1: How are they showing up? Are they being described? And 235 00:15:14,720 --> 00:15:17,640 Speaker 1: for many of your listeners who may have gone to 236 00:15:17,720 --> 00:15:21,040 Speaker 1: film school or you know, screenwriting one on one, you 237 00:15:21,120 --> 00:15:25,240 Speaker 1: don't describe all your characters, which is could which could 238 00:15:25,240 --> 00:15:29,360 Speaker 1: be great? So the banker, you know, the coach, the 239 00:15:29,560 --> 00:15:33,920 Speaker 1: et cetera, they have dialogue, but they may not be described. Well, 240 00:15:34,000 --> 00:15:37,080 Speaker 1: that could be an opportunity to have a discussion to say, okay, 241 00:15:37,120 --> 00:15:40,960 Speaker 1: it's the twenty one century, what could who could the 242 00:15:41,040 --> 00:15:43,560 Speaker 1: coach be? Who could the banker be? And how could 243 00:15:43,600 --> 00:15:49,440 Speaker 1: that be an opportunity to infuse more UH diversity, equity 244 00:15:49,480 --> 00:15:53,000 Speaker 1: inclusion in a piece of content that doesn't disrupt the story. 245 00:15:53,920 --> 00:15:58,440 Speaker 1: It's not changing the authentic truth of of what the 246 00:15:58,760 --> 00:16:03,200 Speaker 1: storyteller a story they were telling. And so that's where 247 00:16:03,800 --> 00:16:08,840 Speaker 1: spell Chuck for bias is being used to help foster 248 00:16:09,000 --> 00:16:12,440 Speaker 1: that conversation and also kind of flag some things, you know, 249 00:16:12,640 --> 00:16:17,480 Speaker 1: especially sensitivities around sexism and racism, and there's some things 250 00:16:17,520 --> 00:16:20,920 Speaker 1: that we can flag. We're all we're all looking to 251 00:16:21,080 --> 00:16:25,840 Speaker 1: be better allies. We're all paying very close attention to 252 00:16:27,480 --> 00:16:30,640 Speaker 1: how things land when you say something right, and this 253 00:16:30,880 --> 00:16:35,480 Speaker 1: is a way to help do them. Let's say a 254 00:16:35,520 --> 00:16:50,120 Speaker 1: quick break out her back. The X Files first premier 255 00:16:50,240 --> 00:16:55,640 Speaker 1: on September, introducing the world to Dr Dana Scully, a 256 00:16:55,760 --> 00:16:58,400 Speaker 1: medical doctor working as a special agent for the FBI, 257 00:16:58,760 --> 00:17:02,960 Speaker 1: alongside her partner Box Molder. I also had pretty big 258 00:17:03,040 --> 00:17:05,520 Speaker 1: crushes on both of them growing up, but that's a 259 00:17:05,560 --> 00:17:09,320 Speaker 1: podcast for another day. Together, they investigate the X files 260 00:17:09,720 --> 00:17:14,720 Speaker 1: unsolved FBI cases caused by unexplained phenomena, whereas Molder is 261 00:17:14,800 --> 00:17:17,359 Speaker 1: the wild card, open to the possibility of the paranormal. 262 00:17:17,720 --> 00:17:22,200 Speaker 1: Fully is capable, efficient, and deeply skeptical, but you also 263 00:17:22,240 --> 00:17:23,600 Speaker 1: get to see a lot of who she is and 264 00:17:23,680 --> 00:17:27,120 Speaker 1: what makes her her. She's a really complex character. For instance, 265 00:17:27,400 --> 00:17:30,760 Speaker 1: her Catholic faith presents an interesting departure from her usual 266 00:17:30,920 --> 00:17:34,399 Speaker 1: skeptical nature. Now, I watched Dana Scully on X Files 267 00:17:34,520 --> 00:17:36,680 Speaker 1: every week growing up, and she was one of the 268 00:17:36,760 --> 00:17:39,440 Speaker 1: first women that I saw and stam regularly, and she 269 00:17:39,600 --> 00:17:42,080 Speaker 1: gave me a way to imagine myself one day working 270 00:17:42,119 --> 00:17:45,320 Speaker 1: in technology too, and it turns out I was not alone. 271 00:17:46,119 --> 00:17:49,040 Speaker 1: The Davis Institute published a report on what they're calling 272 00:17:49,160 --> 00:17:52,080 Speaker 1: the Scully Effect, the idea that Dana Scully inspired a 273 00:17:52,119 --> 00:17:54,720 Speaker 1: generation of women and girls to go into STEM field 274 00:17:54,920 --> 00:17:57,920 Speaker 1: because they finally saw themselves represented. That it has long 275 00:17:58,040 --> 00:18:00,240 Speaker 1: been assumed that Scully had this big and packed on 276 00:18:00,320 --> 00:18:03,199 Speaker 1: women and girls, but before the Davis Institute, there had 277 00:18:03,240 --> 00:18:06,320 Speaker 1: been no formal study confirming it. So here's what they found. 278 00:18:06,560 --> 00:18:08,800 Speaker 1: Women who are medium and heavy watchers of the X 279 00:18:08,880 --> 00:18:12,440 Speaker 1: files hold more positive views of STEM than non or 280 00:18:12,720 --> 00:18:16,640 Speaker 1: light watchers, and that nearly two thirds or of women 281 00:18:16,680 --> 00:18:19,480 Speaker 1: who are familiar with Dana Scully say that she increased 282 00:18:19,480 --> 00:18:22,520 Speaker 1: their beliefs in the importance of STEM, and that among 283 00:18:22,640 --> 00:18:25,399 Speaker 1: women who are familiar with Scully's character, half of them 284 00:18:25,440 --> 00:18:28,760 Speaker 1: report that Scully increase their interest in STEM. So while 285 00:18:28,840 --> 00:18:30,600 Speaker 1: Dana Scully was getting to the bottom of all that 286 00:18:30,640 --> 00:18:34,120 Speaker 1: paranormal activity, she was also inspiring a generation of women 287 00:18:34,119 --> 00:18:37,480 Speaker 1: and girls to be interested in the sciences. And thanks 288 00:18:37,520 --> 00:18:41,280 Speaker 1: to the Davis Institute, well that truth is out there. 289 00:18:42,960 --> 00:18:46,840 Speaker 1: I love how you really highlight that you're able to 290 00:18:46,920 --> 00:18:49,760 Speaker 1: get some data around it. Because I think, like, you 291 00:18:49,840 --> 00:18:51,640 Speaker 1: don't know what you don't know, and so you can't 292 00:18:51,640 --> 00:18:55,440 Speaker 1: even start to understand or tackle the problem if you 293 00:18:55,560 --> 00:18:58,040 Speaker 1: really don't have the information of the scope. And I 294 00:18:58,160 --> 00:19:01,040 Speaker 1: guess that's such a good segue into, uh, the study 295 00:19:01,359 --> 00:19:04,320 Speaker 1: on the Skully effect, because I think for a long 296 00:19:04,440 --> 00:19:08,240 Speaker 1: time it had been just sort of assumed that Dana 297 00:19:08,320 --> 00:19:10,600 Speaker 1: Scully this amazing character on the X Files. It was 298 00:19:10,640 --> 00:19:13,200 Speaker 1: a huge part of my upbringing and development as a 299 00:19:13,280 --> 00:19:15,879 Speaker 1: young person, and I guess still is today. Uh. But 300 00:19:15,960 --> 00:19:19,440 Speaker 1: there was this assumption that certainly a generation of women 301 00:19:19,480 --> 00:19:24,280 Speaker 1: and girls saw this dynamic, successful, capable woman um in 302 00:19:24,520 --> 00:19:28,880 Speaker 1: stem and that she probably had an impact on encouraging 303 00:19:28,920 --> 00:19:31,399 Speaker 1: women and girls to see themselves in these fields and 304 00:19:31,480 --> 00:19:34,240 Speaker 1: then actually go into these fields, but people didn't know. 305 00:19:34,359 --> 00:19:37,240 Speaker 1: I think it was really based on this this assumption 306 00:19:37,359 --> 00:19:39,520 Speaker 1: one can reasonably assume, And so I guess my my 307 00:19:39,640 --> 00:19:42,400 Speaker 1: question would be, why was it important for the Institute 308 00:19:42,480 --> 00:19:45,920 Speaker 1: to really put some research into confirming that, yes, this 309 00:19:46,080 --> 00:19:50,040 Speaker 1: Scully effect did actually make an impact. What was wonderful 310 00:19:50,200 --> 00:19:55,159 Speaker 1: for us is that Fox approached us because the X 311 00:19:55,280 --> 00:19:58,920 Speaker 1: Files was on the air in I think it was 312 00:19:59,040 --> 00:20:01,119 Speaker 1: going off. Then it went off the air. Then it 313 00:20:01,200 --> 00:20:05,360 Speaker 1: was coming back and I think it was um going 314 00:20:05,440 --> 00:20:07,520 Speaker 1: to go off on the air. So it was on 315 00:20:07,680 --> 00:20:09,800 Speaker 1: for what over a decade or so, on and off, 316 00:20:09,960 --> 00:20:14,720 Speaker 1: and they said, you know, we know anecdotally that there's 317 00:20:14,760 --> 00:20:17,800 Speaker 1: this hashtag Scully effect and we want to prove it out. 318 00:20:17,840 --> 00:20:19,639 Speaker 1: Would your partner with us and help us prove that 319 00:20:19,920 --> 00:20:24,080 Speaker 1: theory out? And so what we did is we surveyed 320 00:20:24,800 --> 00:20:29,000 Speaker 1: thousands of women and girls that would have been able 321 00:20:29,119 --> 00:20:33,399 Speaker 1: to watch the show, and we asked them a series 322 00:20:33,440 --> 00:20:36,560 Speaker 1: of questions. And what we found out from that is 323 00:20:37,359 --> 00:20:39,840 Speaker 1: so among the women who said they were familiar with 324 00:20:39,920 --> 00:20:45,240 Speaker 1: the show, said that she was a role model. And 325 00:20:45,440 --> 00:20:47,639 Speaker 1: the other one, which gets up what you were saying, 326 00:20:48,080 --> 00:20:53,080 Speaker 1: six of them said that they work in stem because 327 00:20:53,240 --> 00:20:56,320 Speaker 1: Dannis Scully served as their role model. And when we 328 00:20:56,520 --> 00:21:01,879 Speaker 1: asked them why, it was because she us uh in 329 00:21:02,000 --> 00:21:05,720 Speaker 1: control of her destiny, she didn't have to be saved. 330 00:21:05,960 --> 00:21:11,480 Speaker 1: She used logic, you know, and science to make decisions, 331 00:21:11,640 --> 00:21:17,200 Speaker 1: and she countered a lot of stereotypes for female characters 332 00:21:17,800 --> 00:21:21,080 Speaker 1: you know, at that time, and so it was a 333 00:21:22,280 --> 00:21:26,920 Speaker 1: almost a direct correlation, um and and it was exciting, 334 00:21:27,080 --> 00:21:30,639 Speaker 1: you know for us because normally we wouldn't conduct a 335 00:21:30,760 --> 00:21:34,480 Speaker 1: study on somebody else's I p um and make that 336 00:21:34,520 --> 00:21:38,240 Speaker 1: a case history unless it was something they wanted to partner, 337 00:21:38,680 --> 00:21:42,800 Speaker 1: you know, with us. So we were thrilled to have 338 00:21:42,960 --> 00:21:45,840 Speaker 1: a chance to work with them on that and to 339 00:21:46,200 --> 00:21:52,080 Speaker 1: use that as as evidence. You know, it's definitely evidence. Yeah, 340 00:21:52,240 --> 00:21:55,000 Speaker 1: it's and and it really goes back to this idea 341 00:21:55,119 --> 00:21:57,000 Speaker 1: of if you can see it, you can be it. 342 00:21:57,080 --> 00:22:00,080 Speaker 1: In the importance of that representation. And I know it 343 00:22:00,240 --> 00:22:03,400 Speaker 1: might it might be harder for younger folks to really understand, 344 00:22:03,480 --> 00:22:05,479 Speaker 1: but I was watching The X Files when it when 345 00:22:05,520 --> 00:22:07,679 Speaker 1: it first premiered, I was a big fan. I had 346 00:22:07,840 --> 00:22:09,720 Speaker 1: a poster in my locker that said the truth is 347 00:22:09,760 --> 00:22:14,080 Speaker 1: out there. So I was hugely, hugely involved, like heavily, 348 00:22:14,200 --> 00:22:20,280 Speaker 1: heavily a fan. And you know, the show premiered in nine. Yeah, 349 00:22:20,520 --> 00:22:23,320 Speaker 1: and my my producer and I right before you and 350 00:22:23,359 --> 00:22:26,960 Speaker 1: I started talking, we were trying to name another show 351 00:22:27,440 --> 00:22:29,080 Speaker 1: that was on or a movie that was on the 352 00:22:29,160 --> 00:22:33,480 Speaker 1: air that that involved a woman who was a scientist 353 00:22:33,640 --> 00:22:36,360 Speaker 1: or involved in stem and the only thing I could 354 00:22:36,440 --> 00:22:40,240 Speaker 1: think was the Sandra Bolock movie Um Love Postion number nine. 355 00:22:40,320 --> 00:22:42,560 Speaker 1: She plays a biochemist, but it's kind of it's kind 356 00:22:42,560 --> 00:22:45,000 Speaker 1: of like a romantic comedy. It's not. It's certainly her 357 00:22:45,160 --> 00:22:47,280 Speaker 1: job as a biochemist is not the big part of 358 00:22:47,359 --> 00:22:50,560 Speaker 1: the movie. And you know, today we have so many 359 00:22:50,640 --> 00:22:53,600 Speaker 1: different women, like if you watch Bones or like s Vu, 360 00:22:54,080 --> 00:22:58,000 Speaker 1: women who are in technical fields and in stem. But 361 00:22:58,240 --> 00:23:01,359 Speaker 1: back then they're just really like, like, we struggled to 362 00:23:01,440 --> 00:23:05,680 Speaker 1: come up with another woman scientists on on a film 363 00:23:05,800 --> 00:23:08,600 Speaker 1: or in a television show other than Dana Scully. Back 364 00:23:08,640 --> 00:23:10,800 Speaker 1: in the nineties, I really could not. I could not 365 00:23:10,920 --> 00:23:15,320 Speaker 1: think of any It's so it was a new thing, right, Well, 366 00:23:16,520 --> 00:23:21,320 Speaker 1: maybe we could make a case for the amazing groundbreaking 367 00:23:21,480 --> 00:23:25,760 Speaker 1: Mischelle Nichols. She was on star track, so yes, you 368 00:23:25,880 --> 00:23:30,480 Speaker 1: can assume that she was flying out there in the universe. 369 00:23:31,800 --> 00:23:34,600 Speaker 1: She had to have some kind of STEM degree, right, Yes, 370 00:23:35,359 --> 00:23:37,600 Speaker 1: that's a that's a good that's a good cause. And 371 00:23:37,920 --> 00:23:40,639 Speaker 1: and God bless her and and we're so thrilled with 372 00:23:40,720 --> 00:23:44,880 Speaker 1: everything that she contributed to breaking stereotized. But we couldn't 373 00:23:44,960 --> 00:23:48,040 Speaker 1: say that she was maybe the you know, the first 374 00:23:48,080 --> 00:23:53,440 Speaker 1: scientist because she was in space. That's true. Shout out 375 00:23:53,520 --> 00:23:56,200 Speaker 1: to shout out to her. We love her. Um, I 376 00:23:56,240 --> 00:24:01,000 Speaker 1: guess you know, were you surprised to see how big 377 00:24:01,080 --> 00:24:04,159 Speaker 1: of an impact the Skully effect actually had for a 378 00:24:04,240 --> 00:24:06,920 Speaker 1: generation of women and girls to see themselves reflected instead, Like, 379 00:24:07,040 --> 00:24:10,080 Speaker 1: I find the results to be not surprising, but just 380 00:24:10,240 --> 00:24:13,080 Speaker 1: really kind of affirming that she did have this this 381 00:24:13,320 --> 00:24:15,919 Speaker 1: great role in people being able to see themselves in her, 382 00:24:16,600 --> 00:24:19,119 Speaker 1: not at all, because we've seen other examples of that. 383 00:24:20,000 --> 00:24:24,720 Speaker 1: So another example of that totally different is when Gina 384 00:24:24,840 --> 00:24:29,520 Speaker 1: got to play the first ever president female president on TV. 385 00:24:30,359 --> 00:24:35,080 Speaker 1: I think her show was on nineteen times and the 386 00:24:35,560 --> 00:24:41,239 Speaker 1: Linda Linda Saylor Kaplan Group did a study on uh 387 00:24:41,920 --> 00:24:46,440 Speaker 1: audiences views on the potential of a female president and 388 00:24:47,560 --> 00:24:51,080 Speaker 1: what they found is, I think fifty eight per cent 389 00:24:51,320 --> 00:24:56,280 Speaker 1: of the adults that we're familiar with the show would 390 00:24:56,359 --> 00:25:00,840 Speaker 1: consider a female UM candidate. And this is going back 391 00:25:00,880 --> 00:25:04,200 Speaker 1: to like two thousand four, et cetera. And you think 392 00:25:04,200 --> 00:25:07,080 Speaker 1: about the show was only on you know, nineteen something 393 00:25:07,240 --> 00:25:10,800 Speaker 1: times to have that kind of you know, imprint. And 394 00:25:11,280 --> 00:25:16,320 Speaker 1: and then another great example is UH. You know, Gina 395 00:25:16,800 --> 00:25:22,720 Speaker 1: Uh went out for the Olympics and Archeries. She was 396 00:25:23,640 --> 00:25:28,080 Speaker 1: in the trials and qualified for the trials, and a 397 00:25:28,160 --> 00:25:32,560 Speaker 1: few years ago her coach said, hey, girls, participation of 398 00:25:32,760 --> 00:25:35,520 Speaker 1: archery has just gone crazy. And so we did a 399 00:25:35,640 --> 00:25:38,919 Speaker 1: survey and we worked with the National Association of Archery 400 00:25:39,000 --> 00:25:46,040 Speaker 1: in America, and we found that there were two movies 401 00:25:47,680 --> 00:25:55,880 Speaker 1: in what were those two movies? Hunger Games and what's 402 00:25:55,920 --> 00:26:04,800 Speaker 1: the other one, Rover Brave. Of course, of course those 403 00:26:04,880 --> 00:26:10,919 Speaker 1: two movies came out and girls participation archery went up 404 00:26:11,080 --> 00:26:15,479 Speaker 1: a hundred and four. They they went out and got 405 00:26:15,560 --> 00:26:21,840 Speaker 1: a book. It was instantaneous. Wow. More after a quick break, 406 00:26:32,840 --> 00:26:36,720 Speaker 1: let's get right back into it. One of the questions 407 00:26:36,760 --> 00:26:39,080 Speaker 1: I had, which is a complete like Devil's adig question, 408 00:26:39,119 --> 00:26:41,200 Speaker 1: because you don't need to convince me. But you know, 409 00:26:41,240 --> 00:26:43,359 Speaker 1: when you when you see these people who were like, 410 00:26:43,560 --> 00:26:47,040 Speaker 1: it's just entertainment. You know, it's just movies, it's just TV. 411 00:26:47,480 --> 00:26:49,520 Speaker 1: Doesn't really make a difference. What do you say? What 412 00:26:49,600 --> 00:26:51,399 Speaker 1: do you say to that? Like, what's your response to that? 413 00:26:51,480 --> 00:26:54,160 Speaker 1: Because I have to imagine there has to be people 414 00:26:54,200 --> 00:26:56,359 Speaker 1: out there who think, isn't there something else that you 415 00:26:56,359 --> 00:26:59,000 Speaker 1: should spend your time studying or analyzing or working on. 416 00:26:59,080 --> 00:27:01,480 Speaker 1: Who cares about the television that we consume, with the 417 00:27:01,560 --> 00:27:04,399 Speaker 1: media that we watched, What did you say to that? Well, 418 00:27:04,480 --> 00:27:06,439 Speaker 1: let me give you another example, and let me tell 419 00:27:06,440 --> 00:27:10,080 Speaker 1: you another story. UM, we had the privilege of working 420 00:27:10,160 --> 00:27:14,439 Speaker 1: with UM it was J Walter Thompson at the time. UH, 421 00:27:14,920 --> 00:27:18,440 Speaker 1: And we did a survey of women in nine countries 422 00:27:19,480 --> 00:27:25,720 Speaker 1: and we asked them how they were positively influenced by 423 00:27:25,800 --> 00:27:31,720 Speaker 1: a make believe fictional character, and UH as high as 424 00:27:32,560 --> 00:27:37,000 Speaker 1: one in four said that seeing a positive female role 425 00:27:37,040 --> 00:27:40,600 Speaker 1: model gave them the courage to leave an abusive relationship. Wow, 426 00:27:41,880 --> 00:27:51,880 Speaker 1: that's real. Wow. Yes, I mean that's like saving someone's life, 427 00:27:52,160 --> 00:27:58,320 Speaker 1: Like media has the power to do that exactly. So, yes, 428 00:27:58,400 --> 00:28:02,480 Speaker 1: it's make believe, but what happens in the Maple League 429 00:28:02,520 --> 00:28:06,879 Speaker 1: world can play out hopefully positively in the real world 430 00:28:07,080 --> 00:28:13,320 Speaker 1: and change lives. Oh, I absolutely mean. I don't know 431 00:28:13,440 --> 00:28:15,720 Speaker 1: that I would be hosting a tech podcast if not 432 00:28:15,840 --> 00:28:19,240 Speaker 1: for Dana Scully. You know, I think that there was 433 00:28:19,320 --> 00:28:23,359 Speaker 1: no we just didn't have stories like that where you know, 434 00:28:24,119 --> 00:28:27,720 Speaker 1: her partner was the one who was the the out 435 00:28:27,800 --> 00:28:31,440 Speaker 1: there person. She was the one who was like cool, calm, collected, 436 00:28:31,520 --> 00:28:34,680 Speaker 1: didn't need to be saved, was very capable, and I 437 00:28:34,800 --> 00:28:38,000 Speaker 1: really liked that she was you know, still she still 438 00:28:38,320 --> 00:28:40,280 Speaker 1: you knew that she had a light outside of work, 439 00:28:40,320 --> 00:28:42,080 Speaker 1: and you you you've got to understand her as a 440 00:28:42,440 --> 00:28:45,600 Speaker 1: whole dimensional woman who did this work. I think you 441 00:28:45,680 --> 00:28:48,520 Speaker 1: know if you were you know in the study you 442 00:28:48,600 --> 00:28:50,920 Speaker 1: talked about how like the perception of a scientist was 443 00:28:51,520 --> 00:28:54,320 Speaker 1: an awkward man and a white lab coat. Dana Scully 444 00:28:54,360 --> 00:28:56,320 Speaker 1: wasn't awkward. She did wear a lab coat, but she 445 00:28:56,400 --> 00:28:59,760 Speaker 1: certainly was not awkward. She really they really depicted her 446 00:28:59,800 --> 00:29:02,800 Speaker 1: in loving way and you really got to understand her 447 00:29:02,840 --> 00:29:06,760 Speaker 1: as a a full person. And I think, yeah, I 448 00:29:06,840 --> 00:29:09,640 Speaker 1: just I still remember the first time I watched the 449 00:29:09,680 --> 00:29:11,800 Speaker 1: show and what an impression that made on me, and 450 00:29:12,080 --> 00:29:14,160 Speaker 1: thinking like, oh, maybe this could be a career for 451 00:29:14,240 --> 00:29:16,560 Speaker 1: me one day, maybe I could do something involved in 452 00:29:16,640 --> 00:29:20,800 Speaker 1: technology or science or a stem field. Because she allowed 453 00:29:20,840 --> 00:29:22,600 Speaker 1: me to release see it and I've never seen anything 454 00:29:22,680 --> 00:29:25,880 Speaker 1: like that before. So certainly stories and media have been 455 00:29:26,080 --> 00:29:31,440 Speaker 1: important to me hugely. Absolutely, So we talked about Gina Davis. 456 00:29:32,000 --> 00:29:34,840 Speaker 1: I am just now learning that she is an Olympian 457 00:29:34,960 --> 00:29:38,280 Speaker 1: level archer? Is she as cool as she seems in 458 00:29:38,400 --> 00:29:40,440 Speaker 1: the movie A League of their Own? Like that's the 459 00:29:40,520 --> 00:29:43,280 Speaker 1: benchmark Gina Davis for me, It's like she'll never be 460 00:29:43,400 --> 00:29:47,160 Speaker 1: cooler than that. Movie. Is she just the best? Well, 461 00:29:47,320 --> 00:29:52,840 Speaker 1: what's what's amazing is this summer will be the thirtieth 462 00:29:52,880 --> 00:29:55,400 Speaker 1: anniversary of the League of their Own, Oh my god, 463 00:29:55,680 --> 00:29:59,520 Speaker 1: and you know we're looking to celebrate that plans to 464 00:29:59,640 --> 00:30:08,280 Speaker 1: be shared. And you know, she is sincere, she is dedicated, 465 00:30:08,920 --> 00:30:12,840 Speaker 1: She rolls up her sleeves. There's a lot of celebrities 466 00:30:12,920 --> 00:30:18,840 Speaker 1: that have charities and nonprofits and whatnot, but she really 467 00:30:18,920 --> 00:30:23,960 Speaker 1: digs in. The institute was born out of her vision. Um, 468 00:30:24,880 --> 00:30:29,560 Speaker 1: her desire to change, you know the world and make 469 00:30:29,600 --> 00:30:33,320 Speaker 1: it more equitable, particularly for you know, women and girls. 470 00:30:34,240 --> 00:30:38,120 Speaker 1: I love the world. And um, she's she's a really 471 00:30:38,200 --> 00:30:43,840 Speaker 1: big thinker. She's very creative. Um, but she's very thoughtful, 472 00:30:44,480 --> 00:30:49,280 Speaker 1: you know, and and kind and and as you can tell, 473 00:30:49,440 --> 00:30:55,920 Speaker 1: like on the mouth kind of thing. Um. So yeah, 474 00:30:56,080 --> 00:31:00,560 Speaker 1: she she's that cool and then some I love it, Madeline. 475 00:31:00,720 --> 00:31:02,880 Speaker 1: Is there anything that I did not ask but you 476 00:31:02,920 --> 00:31:05,640 Speaker 1: want to make sure it gets included? Well, yes, So 477 00:31:05,800 --> 00:31:10,760 Speaker 1: we we welcome your audience to become members of the Institute. 478 00:31:10,920 --> 00:31:14,520 Speaker 1: We do year round programming. We have all kinds of 479 00:31:14,600 --> 00:31:20,680 Speaker 1: great events where we launch our studies UM networking. Follow 480 00:31:20,800 --> 00:31:24,680 Speaker 1: us on social media at Gina Davis org and we 481 00:31:24,920 --> 00:31:28,600 Speaker 1: welcome uh for anyone who's listening to get get more 482 00:31:28,640 --> 00:31:33,400 Speaker 1: involved so there are no girls on the Internet. Launched 483 00:31:33,440 --> 00:31:36,680 Speaker 1: a new newsletter called Dear Internet, where I am taking 484 00:31:36,720 --> 00:31:39,800 Speaker 1: your Internet questions and conundrums. You can subscribe for free. 485 00:31:39,800 --> 00:31:41,720 Speaker 1: It can go be dot com slash newsletter to be 486 00:31:41,840 --> 00:31:44,400 Speaker 1: my full advice and submit your own questions. You might 487 00:31:44,480 --> 00:31:46,800 Speaker 1: even hear yours on the show. So I wanted to 488 00:31:46,840 --> 00:31:48,440 Speaker 1: read one of the letters that we got from a 489 00:31:48,480 --> 00:31:52,600 Speaker 1: listener that we're calling Joe Dear Internet. I have dealt 490 00:31:52,680 --> 00:31:54,640 Speaker 1: with depression on and off for most of my life, 491 00:31:55,000 --> 00:31:57,760 Speaker 1: and since the pandemic, my issues have only gotten more pronounced. 492 00:31:58,080 --> 00:32:00,640 Speaker 1: I experienced a particularly low period where if I was 493 00:32:00,680 --> 00:32:02,360 Speaker 1: able to get out of bed and dressed, it was 494 00:32:02,440 --> 00:32:05,560 Speaker 1: a good day. When I feel like this, my default 495 00:32:05,640 --> 00:32:07,880 Speaker 1: is to cut off almost everyone and everything from the 496 00:32:07,960 --> 00:32:11,160 Speaker 1: outside world. Thanks to a good therapist and new medication, 497 00:32:11,400 --> 00:32:13,880 Speaker 1: that period of my life is thankfully behind me for now. 498 00:32:14,640 --> 00:32:17,320 Speaker 1: But now that I'm reconnecting with the world, I'm struggling 499 00:32:17,400 --> 00:32:20,120 Speaker 1: to deal with email inbox that is full of unanswered emails, 500 00:32:20,480 --> 00:32:23,120 Speaker 1: some of which are or at least word time sensitive. 501 00:32:23,560 --> 00:32:26,320 Speaker 1: The idea of tackling this inbox fills me with dread. 502 00:32:26,760 --> 00:32:29,160 Speaker 1: It feels really embarrassing and almost rude to reply to 503 00:32:29,200 --> 00:32:32,280 Speaker 1: an email after several months. These feelings of shame and 504 00:32:32,360 --> 00:32:34,440 Speaker 1: guilt make it difficult to move forward. I'm cleaning on 505 00:32:34,520 --> 00:32:37,680 Speaker 1: my inbox? What's the cutoff for responding to an email late? 506 00:32:38,200 --> 00:32:40,560 Speaker 1: Are there instances where a reply can come so late 507 00:32:40,640 --> 00:32:42,840 Speaker 1: that it's better to not reply at all? And I 508 00:32:42,920 --> 00:32:45,640 Speaker 1: need to exibling that people want My response is so overdue. 509 00:32:47,000 --> 00:32:48,720 Speaker 1: So if you want to read my full response, subscribe 510 00:32:48,760 --> 00:32:51,240 Speaker 1: to our newsletter. But yeah, I really wanted to start 511 00:32:51,360 --> 00:32:54,800 Speaker 1: with this letter because oh boy, can I relate? And 512 00:32:54,880 --> 00:32:57,480 Speaker 1: I feel like a lot of us, especially since the pandemic, 513 00:32:57,600 --> 00:33:01,200 Speaker 1: can probably relate to this feeling of dread and a 514 00:33:01,320 --> 00:33:03,760 Speaker 1: keyness that comes with having to dig out an inbox. 515 00:33:04,240 --> 00:33:06,440 Speaker 1: There's actually a recent episode of one of my favorite 516 00:33:06,440 --> 00:33:09,000 Speaker 1: ever podcasts, You're Wrong About, about how email got to 517 00:33:09,040 --> 00:33:12,000 Speaker 1: be so awful and annoying? Uh spoiler alert. It has 518 00:33:12,040 --> 00:33:14,080 Speaker 1: a lot to do with the rise of Gmail as 519 00:33:14,080 --> 00:33:17,600 Speaker 1: our email platform, and tech companies like Google kind of 520 00:33:17,680 --> 00:33:21,280 Speaker 1: thinking that everyone wants to enjoy the pressures and expectations 521 00:33:21,760 --> 00:33:25,000 Speaker 1: to respond to emails right away no matter what time 522 00:33:25,080 --> 00:33:27,040 Speaker 1: it is that people who are hit tech companies have 523 00:33:27,120 --> 00:33:30,000 Speaker 1: to deal with. So that ikey feeling that comes with 524 00:33:30,160 --> 00:33:33,480 Speaker 1: your email inbox is actually kind of by design, So 525 00:33:34,080 --> 00:33:38,160 Speaker 1: thank you Google. Uh you know, I also deal with 526 00:33:38,240 --> 00:33:41,160 Speaker 1: mental health issues. In my case, I have generalized anxiety disordered, 527 00:33:41,400 --> 00:33:45,320 Speaker 1: and my anxiety can really manifest itself into feeling panicky 528 00:33:45,480 --> 00:33:48,320 Speaker 1: around email in my inbox, and at least to things 529 00:33:48,360 --> 00:33:50,920 Speaker 1: like me ignoring emails and really letting them pile up, 530 00:33:51,400 --> 00:33:54,480 Speaker 1: or spending an hour overthinking an email response that just 531 00:33:54,560 --> 00:33:57,880 Speaker 1: says sounds good, thanks, you know, I'll spend an hour 532 00:33:58,120 --> 00:33:59,880 Speaker 1: writing that and thinking about all the ways they can 533 00:33:59,880 --> 00:34:01,600 Speaker 1: go wrong, most of which are just kind of in 534 00:34:01,680 --> 00:34:05,800 Speaker 1: my head. But you know, I'm also a busy creative professional. 535 00:34:05,920 --> 00:34:08,000 Speaker 1: I make a podcast and I do other things, and 536 00:34:08,080 --> 00:34:11,520 Speaker 1: so I can't let my email get two out of control, 537 00:34:11,600 --> 00:34:14,840 Speaker 1: and so I actually take really clear steps to handle 538 00:34:14,880 --> 00:34:17,480 Speaker 1: this in my own life. I have someone who handles 539 00:34:17,520 --> 00:34:19,400 Speaker 1: my inbox because I know that I just cannot be 540 00:34:19,520 --> 00:34:22,000 Speaker 1: trusted and really should not be trusted with my own email. 541 00:34:22,360 --> 00:34:26,640 Speaker 1: And I'm really curious, what is your personal relationship with email? 542 00:34:26,680 --> 00:34:28,120 Speaker 1: What does it look like, what does it feel like. 543 00:34:28,719 --> 00:34:30,600 Speaker 1: Are you someone who feels the need to get to 544 00:34:30,680 --> 00:34:33,919 Speaker 1: inbox zero? Are you somebody like me who when other 545 00:34:33,960 --> 00:34:36,040 Speaker 1: people look at your inbox they kind of cower and 546 00:34:36,160 --> 00:34:39,560 Speaker 1: disgust at how many unread emails you have? What does 547 00:34:39,600 --> 00:34:41,640 Speaker 1: it look like? I really want to know, So subscribe 548 00:34:41,680 --> 00:34:44,000 Speaker 1: to our newsletter at tangodi dot com slash newsletter and 549 00:34:44,120 --> 00:34:45,759 Speaker 1: let me know. I can't wait to hear from you. 550 00:34:49,080 --> 00:34:51,120 Speaker 1: Got a story about an interesting thing in tech, or 551 00:34:51,160 --> 00:34:53,000 Speaker 1: just want to say hi? You can be just at 552 00:34:53,040 --> 00:34:55,800 Speaker 1: Hello at tangodi dot com. You can also find transcripts 553 00:34:55,840 --> 00:34:58,560 Speaker 1: today's episode at tangodi dot com. There Are No Girls 554 00:34:58,600 --> 00:35:00,920 Speaker 1: on the Internet was created by me H Todd. It's 555 00:35:00,960 --> 00:35:04,240 Speaker 1: a production of iHeart Radio and Unboss creative Jonathan Strickland 556 00:35:04,239 --> 00:35:06,920 Speaker 1: as our executive producer. Terry Harrison is our producer and 557 00:35:07,000 --> 00:35:10,680 Speaker 1: sound engineer. Michael Amato is our contributing producer. I'm your host, 558 00:35:10,800 --> 00:35:13,560 Speaker 1: Bridget Todd. If you want to help us grow, rate 559 00:35:13,640 --> 00:35:16,719 Speaker 1: and review us on Apple Podcasts. For more podcasts from 560 00:35:16,760 --> 00:35:19,360 Speaker 1: iHeart Radio, check out the iHeart Radio app, Apple podcast, 561 00:35:19,440 --> 00:35:20,680 Speaker 1: or wherever you get your podcasts.