1 00:00:03,200 --> 00:00:06,240 Speaker 1: Welcome to Stuff Mom Never Told You from how stupp 2 00:00:06,240 --> 00:00:14,120 Speaker 1: Works dot com. Hello, and welcome to the podcast. I'm 3 00:00:14,160 --> 00:00:18,200 Speaker 1: Caroline and I'm Christen, and today marks the final episode 4 00:00:18,239 --> 00:00:21,840 Speaker 1: in our fourth part STEMS series looking at women in 5 00:00:21,880 --> 00:00:27,440 Speaker 1: the fields of science, technology, engineering and mathematics. Yes, it's 6 00:00:27,600 --> 00:00:30,600 Speaker 1: math day here on Stuff Mom Never Told You. And 7 00:00:30,640 --> 00:00:33,639 Speaker 1: as we've been doing with the other STEM conversations, we've 8 00:00:33,680 --> 00:00:38,040 Speaker 1: been having, let's first look a little bit at history, 9 00:00:38,560 --> 00:00:42,479 Speaker 1: because for math in particular, it goes back quite a ways, 10 00:00:42,680 --> 00:00:46,240 Speaker 1: all the way to ancient Alexandria with Hypatia, who is 11 00:00:46,280 --> 00:00:51,440 Speaker 1: one of the earliest and certainly best known female mathematicians. Yeah, 12 00:00:51,479 --> 00:00:54,120 Speaker 1: her dad, Theon was one of the last members of 13 00:00:54,160 --> 00:00:56,960 Speaker 1: the Library of Alexandria, and she followed in his footstep 14 00:00:57,000 --> 00:01:01,480 Speaker 1: studying math and astronomy. Um. She didn't actually have a 15 00:01:01,520 --> 00:01:05,000 Speaker 1: happy ending though, Yeah, she was eventually beaten to death 16 00:01:05,080 --> 00:01:08,880 Speaker 1: with roofing tiles by an angry mob of religious zealots. 17 00:01:09,760 --> 00:01:13,880 Speaker 1: But thankfully things have improved. I mean we should say though, 18 00:01:13,920 --> 00:01:17,840 Speaker 1: that Hypatia was so notable for the work and the 19 00:01:17,880 --> 00:01:20,640 Speaker 1: scholarship that she was doing at the time, and I 20 00:01:20,680 --> 00:01:23,920 Speaker 1: don't think it was that she was a woman studying 21 00:01:24,600 --> 00:01:29,120 Speaker 1: math and astronomy that got her killed by an angry 22 00:01:29,160 --> 00:01:31,400 Speaker 1: religious mob, but more the fact that she was an 23 00:01:31,400 --> 00:01:36,440 Speaker 1: academic and academia was a bit at odds with obviously 24 00:01:36,640 --> 00:01:43,119 Speaker 1: religious zelotry. Indeed, while moving forward, we have Winnifred Edgerton Meryll. 25 00:01:43,200 --> 00:01:45,600 Speaker 1: She was the first American woman awarded a PhD in 26 00:01:45,680 --> 00:01:49,360 Speaker 1: math from Columbia University. But this was not as recent 27 00:01:49,400 --> 00:01:52,960 Speaker 1: as you might think. This was an eight Yeah, the 28 00:01:53,000 --> 00:01:56,720 Speaker 1: board of Columbia initially denied her request to pursue a 29 00:01:56,760 --> 00:02:00,480 Speaker 1: pH d, of course, but in her open to Wary 30 00:02:00,920 --> 00:02:03,440 Speaker 1: it was funny how math was described as a quote 31 00:02:03,480 --> 00:02:07,120 Speaker 1: unquote masculine pursuit of hers. And even though she had 32 00:02:07,160 --> 00:02:11,880 Speaker 1: to overcome that initial obstacle at Columbia, she did gain 33 00:02:11,919 --> 00:02:14,040 Speaker 1: a lot of notoriety at the time. She was very 34 00:02:14,080 --> 00:02:18,359 Speaker 1: well respected in her day, and important to our conversation 35 00:02:18,480 --> 00:02:22,040 Speaker 1: about women and diversity is Euphemia Lufton Haynes, who's the 36 00:02:22,080 --> 00:02:24,600 Speaker 1: first African American woman to earn a PhD in math 37 00:02:24,639 --> 00:02:28,720 Speaker 1: in ninety three from Catholic University. And by that time 38 00:02:28,960 --> 00:02:32,560 Speaker 1: many black women were earning masters in math. But if 39 00:02:32,600 --> 00:02:34,760 Speaker 1: you think about it, though, we have Meryl earning that 40 00:02:34,840 --> 00:02:38,519 Speaker 1: first PhD in eight six, and it's not until ninety 41 00:02:38,600 --> 00:02:41,280 Speaker 1: three that we have Haynes earning the same degree. And 42 00:02:41,560 --> 00:02:44,080 Speaker 1: that's something that will come up again where there is 43 00:02:44,760 --> 00:02:48,480 Speaker 1: that gap not just gender wise with math, but also 44 00:02:48,639 --> 00:02:53,560 Speaker 1: in terms of socio economic and ethnic backgrounds. But then 45 00:02:53,560 --> 00:02:55,400 Speaker 1: if we want to fast forward to today, just for 46 00:02:55,440 --> 00:02:59,000 Speaker 1: an example of a woman who's doing really amazing things 47 00:02:59,000 --> 00:03:03,160 Speaker 1: with math. One of the two thousand thirteen McArthur Fellows 48 00:03:03,800 --> 00:03:09,880 Speaker 1: is statistician Susan Murphy, and she collects and develops methodologies 49 00:03:09,919 --> 00:03:12,960 Speaker 1: to evaluate courses of treatment for people who are coping 50 00:03:13,000 --> 00:03:17,200 Speaker 1: with chronic and relapsing disorders, especially things like substance abuse 51 00:03:17,600 --> 00:03:21,919 Speaker 1: and depression. And she wanted to have an impact on 52 00:03:22,360 --> 00:03:24,440 Speaker 1: real life, kind of like what we were talking about 53 00:03:24,440 --> 00:03:28,079 Speaker 1: in the engineering episode. She really sees an altruistic way 54 00:03:28,120 --> 00:03:32,799 Speaker 1: to use statistics to improve people's lives. Man, I took 55 00:03:32,840 --> 00:03:35,880 Speaker 1: statistics in college my my freshman year, and that was 56 00:03:37,640 --> 00:03:41,480 Speaker 1: different from the other things that I enjoyed doing. Yeah, 57 00:03:41,480 --> 00:03:43,600 Speaker 1: I took a statistics course as well. It was the 58 00:03:43,720 --> 00:03:47,760 Speaker 1: one math class I was required to take in college 59 00:03:47,760 --> 00:03:51,160 Speaker 1: as a journalism major, and it was a summer statistics course, 60 00:03:51,800 --> 00:03:56,320 Speaker 1: students would show up in their swimsuits. They're basically on 61 00:03:56,360 --> 00:04:01,880 Speaker 1: a pool break to come to class, and yeah, it 62 00:04:01,960 --> 00:04:05,320 Speaker 1: was the imperative to save lives. I'll say that basic 63 00:04:05,360 --> 00:04:08,400 Speaker 1: statistics course wasn't there, but it was. It was still 64 00:04:08,440 --> 00:04:11,720 Speaker 1: neat to to learn those things. But what Murphy does 65 00:04:11,800 --> 00:04:15,840 Speaker 1: she calls them just in time adaptive interventions. We were 66 00:04:15,880 --> 00:04:18,720 Speaker 1: not learning that in my statistics course. But I will 67 00:04:18,720 --> 00:04:21,160 Speaker 1: say though, that what what she does is a neat 68 00:04:21,200 --> 00:04:26,320 Speaker 1: application of how math also intersects with psychiatry, mental health, 69 00:04:26,360 --> 00:04:31,240 Speaker 1: and also social programs. Absolutely. Also, speaking of those different 70 00:04:31,240 --> 00:04:34,680 Speaker 1: avenues for pursuing a career, let's look at some of 71 00:04:34,680 --> 00:04:38,440 Speaker 1: the fields that math majors enter. Those can include teaching, 72 00:04:39,080 --> 00:04:42,240 Speaker 1: finance and economics, or you know, like Murphy, you can 73 00:04:42,240 --> 00:04:45,640 Speaker 1: be a statistician. Yeah, there's also actuarial science, which is 74 00:04:45,720 --> 00:04:50,960 Speaker 1: analyzing statistics to calculate the probabilities of things like disability, unemployment, etcetera. 75 00:04:51,480 --> 00:04:53,800 Speaker 1: And there's also computer science that goes along with it, 76 00:04:54,040 --> 00:05:02,200 Speaker 1: operations research, cryptography, which is neat um budget and analysis, ease, ecology, UM. 77 00:05:02,200 --> 00:05:07,000 Speaker 1: And if you go into a math specialty, probably outside 78 00:05:07,000 --> 00:05:10,359 Speaker 1: of just basic teaching, you can earn a decent income 79 00:05:10,440 --> 00:05:16,480 Speaker 1: for sure. Yeah, the math specialists median income comes to thousand, 80 00:05:16,560 --> 00:05:20,720 Speaker 1: three hundred and fifty dollars. That was in with growth 81 00:05:20,760 --> 00:05:23,919 Speaker 1: expected in that sector. And so it makes me sad 82 00:05:24,000 --> 00:05:26,000 Speaker 1: that I you know, when I was in high school, 83 00:05:26,040 --> 00:05:27,640 Speaker 1: I looked my math teacher in the face and I 84 00:05:27,680 --> 00:05:30,800 Speaker 1: was like, I am never taking any more math. I 85 00:05:30,839 --> 00:05:34,400 Speaker 1: feel like throughout this entire STEM series, Caroline, you and 86 00:05:34,440 --> 00:05:38,040 Speaker 1: I both with our liberal arts background, both as journalism 87 00:05:38,160 --> 00:05:41,760 Speaker 1: majors in college, and I have spent some reflective times 88 00:05:42,240 --> 00:05:47,040 Speaker 1: thinking about what could have been if those scientific mathematic 89 00:05:47,120 --> 00:05:49,880 Speaker 1: seeds had maybe been planted and watered a little a 90 00:05:49,920 --> 00:05:53,560 Speaker 1: little more diligently back in the day. But um, if 91 00:05:53,600 --> 00:05:57,919 Speaker 1: we look though at the math pipeline, of all of 92 00:05:58,080 --> 00:06:03,440 Speaker 1: the letters in the M alphabet, the M is doing 93 00:06:03,480 --> 00:06:08,159 Speaker 1: the best. Yeah. Um, women earn of the math and 94 00:06:08,240 --> 00:06:12,919 Speaker 1: statistics bachelor's degrees, but they make up just twent of 95 00:06:13,080 --> 00:06:16,200 Speaker 1: math pH d s. So there's that pipeline theory again, 96 00:06:16,200 --> 00:06:19,200 Speaker 1: that you're losing women as they get older and go 97 00:06:19,320 --> 00:06:23,360 Speaker 1: through the educational system. Yeah, and similarly, women comprise only 98 00:06:23,440 --> 00:06:29,039 Speaker 1: twenty of the computer science and math profession workforce. So 99 00:06:29,240 --> 00:06:34,480 Speaker 1: kind of like with the situation for engineering, plenty of 100 00:06:34,480 --> 00:06:37,360 Speaker 1: bachelor's degrees, but then there's that major drop off once 101 00:06:37,400 --> 00:06:41,280 Speaker 1: you get into the real world. And unfortunately, in real 102 00:06:41,279 --> 00:06:45,200 Speaker 1: world terms, new census data shows that that is a 103 00:06:45,320 --> 00:06:50,000 Speaker 1: drop from a thirty four percent high in I wonder 104 00:06:50,080 --> 00:06:54,800 Speaker 1: what happened, mathematician people, Can you tell us Maybe we 105 00:06:54,800 --> 00:06:57,960 Speaker 1: need some actually real scientists to maybe dig through these 106 00:06:58,000 --> 00:07:00,600 Speaker 1: stats for us exactly. But if you look just at 107 00:07:00,640 --> 00:07:05,040 Speaker 1: mathematics careers, women's participation is actually up to fort from 108 00:07:05,080 --> 00:07:10,400 Speaker 1: just in nine. Yeah, so maybe the computer science gender 109 00:07:10,440 --> 00:07:16,840 Speaker 1: gap is pulling down that overall statistic um. But one 110 00:07:16,880 --> 00:07:21,240 Speaker 1: of the most persistent stereotypes that is talked about when 111 00:07:21,320 --> 00:07:24,880 Speaker 1: it comes to engaging girls in STEM is this idea 112 00:07:25,080 --> 00:07:29,440 Speaker 1: that girls aren't good at math. And so even though 113 00:07:29,480 --> 00:07:33,360 Speaker 1: there are a lot of women who are pursuing these degrees, 114 00:07:33,600 --> 00:07:37,160 Speaker 1: there is still this debate that circles back around to 115 00:07:37,880 --> 00:07:41,640 Speaker 1: this basic question of whether or not our brains are 116 00:07:41,720 --> 00:07:48,440 Speaker 1: as good with numbers compared to with words. Yeah, I mean, 117 00:07:48,800 --> 00:07:53,240 Speaker 1: that's a whole bunch of huey. Although I say that, 118 00:07:53,360 --> 00:07:55,400 Speaker 1: and then I'm going to admit that I was, I 119 00:07:55,440 --> 00:07:59,000 Speaker 1: have been terrible at math. My entire life. I had 120 00:07:59,040 --> 00:08:04,600 Speaker 1: tutors forever. Um I just I never it never really 121 00:08:04,760 --> 00:08:07,239 Speaker 1: clicked with me. But that's so in the same way 122 00:08:07,360 --> 00:08:10,440 Speaker 1: that you have a guy who can say the same thing, 123 00:08:10,880 --> 00:08:14,480 Speaker 1: who might be more of a word person. Thankfully, you 124 00:08:14,560 --> 00:08:17,200 Speaker 1: and I don't have to represent the entire population. But 125 00:08:17,280 --> 00:08:20,840 Speaker 1: when you average everything out and look at all of 126 00:08:20,920 --> 00:08:26,000 Speaker 1: girls versus all of boys, you see those generalizations, those 127 00:08:26,040 --> 00:08:32,720 Speaker 1: assumed gender differences start to disappear. And Elizabeth Spelki is 128 00:08:32,760 --> 00:08:35,480 Speaker 1: someone who we brought up in our first conversation on 129 00:08:35,559 --> 00:08:39,719 Speaker 1: Women and Science, and she is a Harvard psychologist and 130 00:08:40,280 --> 00:08:44,400 Speaker 1: h studies a lot about cognitive development in babies, and 131 00:08:44,559 --> 00:08:47,560 Speaker 1: she performed a meta analysis which found that babies as 132 00:08:47,559 --> 00:08:52,280 Speaker 1: young as six months performed equally well on adding and subtracting. Yes, 133 00:08:52,320 --> 00:08:56,760 Speaker 1: that's right. Infants can do their own very rudiment reforms 134 00:08:57,200 --> 00:09:03,479 Speaker 1: of addition and subtraction. And from those earliest stages, Spelkie says, 135 00:09:03,559 --> 00:09:07,400 Speaker 1: no difference between the male baby brain and the female 136 00:09:07,440 --> 00:09:10,920 Speaker 1: baby brain. So you know, we have these infants doing 137 00:09:10,920 --> 00:09:13,400 Speaker 1: all of that crazy infant math, which is which is 138 00:09:13,440 --> 00:09:16,880 Speaker 1: crazy time for me. I think that's interesting. That's adorable 139 00:09:17,000 --> 00:09:19,600 Speaker 1: and adorable I wonder what they're adding, like how many 140 00:09:19,679 --> 00:09:22,480 Speaker 1: milk bottles. I don't know why they have a high 141 00:09:22,520 --> 00:09:26,560 Speaker 1: pitched voice anyway, So boys and girls do perform equally 142 00:09:26,559 --> 00:09:31,079 Speaker 1: well in math until they start getting higher up in school, 143 00:09:31,160 --> 00:09:36,600 Speaker 1: basically around puberty time. Yeah, there are differences that emerge. 144 00:09:36,640 --> 00:09:40,000 Speaker 1: For instance, girls tend to be better at computation, whereas 145 00:09:40,040 --> 00:09:44,079 Speaker 1: boys tend to be better at problem solving. But when 146 00:09:44,440 --> 00:09:47,600 Speaker 1: researchers look at what's going on outside of school, they 147 00:09:47,640 --> 00:09:53,280 Speaker 1: think that perhaps these gender differences in our extracurricular activities 148 00:09:53,679 --> 00:10:00,200 Speaker 1: might simply be socializing and preparing boys better for math fields. Right. 149 00:10:00,200 --> 00:10:04,760 Speaker 1: And so boys were more likely to participate in stimulated activities, 150 00:10:04,880 --> 00:10:07,880 Speaker 1: conduct at home science experiments, and spend time on the 151 00:10:07,920 --> 00:10:11,559 Speaker 1: computer and own a calculator. I only recently ps got 152 00:10:11,640 --> 00:10:14,679 Speaker 1: rid of my t I don't let my parents hear 153 00:10:14,760 --> 00:10:18,840 Speaker 1: this podcast, um, whereas girls spent more time on math 154 00:10:18,840 --> 00:10:22,200 Speaker 1: homework itself. And that again, that's that pattern that we 155 00:10:22,600 --> 00:10:24,360 Speaker 1: see over and over and over again, and all of 156 00:10:24,400 --> 00:10:28,080 Speaker 1: these disciplines is girls are very diligent with their schoolwork, right, 157 00:10:28,200 --> 00:10:31,679 Speaker 1: they're very we're very good at making the grade, at 158 00:10:31,720 --> 00:10:34,320 Speaker 1: studying and sitting down and doing these things, whereas boys 159 00:10:34,440 --> 00:10:39,160 Speaker 1: might just be more actively engaged with it on more 160 00:10:39,200 --> 00:10:43,480 Speaker 1: of a comprehensive level. For instance, the percentage of girls 161 00:10:43,520 --> 00:10:48,920 Speaker 1: taking pre calculus slash analysis at was higher actually than 162 00:10:49,000 --> 00:10:52,280 Speaker 1: the percentage of high school guys at who were taking 163 00:10:52,600 --> 00:10:56,280 Speaker 1: those same classes. Right, And the same trend holds true 164 00:10:56,280 --> 00:10:58,720 Speaker 1: for the percent of girls taking algebra two, which was 165 00:10:58,760 --> 00:11:03,040 Speaker 1: seventy eight compared to guys. But an equal percentage of 166 00:11:03,080 --> 00:11:07,720 Speaker 1: males and females in high school took calculus at. Yeah. 167 00:11:07,720 --> 00:11:10,880 Speaker 1: In a side note, and I'm not entirely sure how 168 00:11:11,679 --> 00:11:16,720 Speaker 1: this necessarily relates directly to the gender gap, but there 169 00:11:16,840 --> 00:11:20,720 Speaker 1: was one finding that girls who take calculus are three 170 00:11:20,760 --> 00:11:26,240 Speaker 1: times more likely to study math in college. So I'm 171 00:11:26,280 --> 00:11:28,000 Speaker 1: not entirely sure, but I thought that was that was 172 00:11:28,080 --> 00:11:30,920 Speaker 1: kind of interesting. It's like an interesting predictor. But when 173 00:11:30,920 --> 00:11:35,640 Speaker 1: you look at the performance overall of boys and girls 174 00:11:35,720 --> 00:11:39,079 Speaker 1: on math exams, it's usually you know a bell curve 175 00:11:39,160 --> 00:11:42,520 Speaker 1: where you have the best in the brightest and then 176 00:11:42,600 --> 00:11:45,160 Speaker 1: the not so great at math on the on the 177 00:11:45,200 --> 00:11:48,800 Speaker 1: other end, and you have more boys populated on either 178 00:11:49,000 --> 00:11:52,600 Speaker 1: end of that math bell curve, So you have a 179 00:11:52,760 --> 00:11:55,800 Speaker 1: lot of guys, more guys in fact, who are really 180 00:11:55,800 --> 00:11:58,320 Speaker 1: really really really good at math. But you have more 181 00:11:58,360 --> 00:12:01,120 Speaker 1: guys than girls who are really really really really not 182 00:12:01,240 --> 00:12:04,840 Speaker 1: so good at math. So they're thinking that that high 183 00:12:04,960 --> 00:12:08,160 Speaker 1: end of the Bell curve, with that pocket of math 184 00:12:08,280 --> 00:12:12,679 Speaker 1: savants are skewing our perception to say that, oh, well, 185 00:12:12,679 --> 00:12:15,800 Speaker 1: guys just better than math math, whereas it's like, no, actually, 186 00:12:15,920 --> 00:12:19,000 Speaker 1: guys are just more spread out, they have more of 187 00:12:19,040 --> 00:12:23,400 Speaker 1: the math performance extremes. Interesting, well, I mean, speaking of gaps, 188 00:12:23,480 --> 00:12:26,160 Speaker 1: let's look at socio economics and ethnicity, which is a 189 00:12:26,200 --> 00:12:30,600 Speaker 1: factor that you mentioned earlier. Kristen Um studies have found 190 00:12:30,600 --> 00:12:33,400 Speaker 1: that white and Asian Pacific Islander students and those from 191 00:12:33,520 --> 00:12:38,079 Speaker 1: higher income families post significantly higher scores than their counterparts 192 00:12:38,120 --> 00:12:41,839 Speaker 1: who are black, Hispanic, or American Indian, or who are 193 00:12:41,880 --> 00:12:46,240 Speaker 1: from lower income families. And you're gonna see that reflected 194 00:12:46,280 --> 00:12:50,199 Speaker 1: in the low numbers of non white American women in math. 195 00:12:50,240 --> 00:12:54,160 Speaker 1: They're earning only five per cent of the bachelor's in math. 196 00:12:54,240 --> 00:12:56,800 Speaker 1: And so if you want an argument for maybe some 197 00:12:56,920 --> 00:13:01,199 Speaker 1: more social or environmental things that will contribute to those 198 00:13:01,280 --> 00:13:06,400 Speaker 1: gaps and learning, this is that because if you look 199 00:13:06,520 --> 00:13:10,120 Speaker 1: at those same ethnicities, if you go to different countries, 200 00:13:10,520 --> 00:13:14,920 Speaker 1: you can see that nature versus nurture argument being one 201 00:13:15,080 --> 00:13:19,960 Speaker 1: fully in favor of nurture. Absolutely, And one example of 202 00:13:20,000 --> 00:13:24,080 Speaker 1: this is Asian American students. It's a cultural thing. And 203 00:13:24,080 --> 00:13:26,160 Speaker 1: if you like, if you even think back to our 204 00:13:26,360 --> 00:13:30,400 Speaker 1: Tiger Mom episode that we did, when a culture and 205 00:13:30,640 --> 00:13:36,000 Speaker 1: or a family unit individually, UM emphasizes the importance of 206 00:13:36,120 --> 00:13:39,760 Speaker 1: education of whatever subject or education in general, you see 207 00:13:39,800 --> 00:13:44,559 Speaker 1: better performance. So, speaking of nature versus nurture, Use from 208 00:13:44,559 --> 00:13:46,520 Speaker 1: the University of Michigan told The New York Times that 209 00:13:46,559 --> 00:13:49,560 Speaker 1: there is good survey data showing that this disbelief and 210 00:13:49,600 --> 00:13:52,600 Speaker 1: innate ability and the conviction that math achievement can be 211 00:13:52,640 --> 00:13:56,880 Speaker 1: improved through practice is a tremendous cultural asset in Asian 212 00:13:56,920 --> 00:14:00,839 Speaker 1: society and among Asian Americans. Yeah, and he was talking 213 00:14:00,840 --> 00:14:03,840 Speaker 1: about how if you perform poorly in math, guess what, 214 00:14:03,960 --> 00:14:07,920 Speaker 1: your parents are simply going to push you to perform 215 00:14:08,080 --> 00:14:11,800 Speaker 1: better because they have more of a growth mindset, thinking 216 00:14:11,840 --> 00:14:15,400 Speaker 1: that that math isn't just some innate ability that you 217 00:14:15,440 --> 00:14:18,920 Speaker 1: are born with by virtue of your X or Y chromosomes, 218 00:14:18,920 --> 00:14:21,800 Speaker 1: but rather something that you can learn and adapt. UM. 219 00:14:21,960 --> 00:14:26,440 Speaker 1: In side note, Icelandic girls, we have any Icelandic listeners 220 00:14:27,080 --> 00:14:30,800 Speaker 1: girls and you on Nation are apparently math geniuses and 221 00:14:30,840 --> 00:14:33,560 Speaker 1: they're not entirely sure why. But when they were looking 222 00:14:33,600 --> 00:14:40,040 Speaker 1: at global math exam numbers, Icelandic girls outperforming. That is interesting. 223 00:14:40,040 --> 00:14:42,040 Speaker 1: I wonder, I wonder if that has to do with, 224 00:14:42,600 --> 00:14:45,840 Speaker 1: you know, maybe different gender roles. Yeah, because when you 225 00:14:45,880 --> 00:14:49,600 Speaker 1: move to Scandinavia, a lot of times you have places 226 00:14:49,640 --> 00:14:54,880 Speaker 1: like Norway, Iceland, Sweden that always consistently rank as being 227 00:14:55,240 --> 00:14:58,960 Speaker 1: the most female friendly or female friendliest. I should say 228 00:14:59,360 --> 00:15:04,240 Speaker 1: that group radically correct. Um. But in Iceland, in Japan, 229 00:15:04,760 --> 00:15:08,840 Speaker 1: all around the world, girls anxieties about being good enough 230 00:15:08,880 --> 00:15:12,360 Speaker 1: in math do hold steady from country to country. In 231 00:15:12,400 --> 00:15:16,280 Speaker 1: other words, even though Icelanded girls, for instance, are performing 232 00:15:16,480 --> 00:15:21,240 Speaker 1: really well, they will report higher concerns about well, maybe 233 00:15:21,240 --> 00:15:24,000 Speaker 1: they're not good enough. Yeah. Well, I mean that does 234 00:15:24,120 --> 00:15:27,200 Speaker 1: make me, speaking of self reflection through this STEM series, 235 00:15:27,280 --> 00:15:30,000 Speaker 1: that does make me think back to my math experiences. 236 00:15:30,320 --> 00:15:33,560 Speaker 1: And there was such a well, not that my family 237 00:15:33,680 --> 00:15:36,160 Speaker 1: was like you know, they were slave drivers or anything, 238 00:15:36,480 --> 00:15:38,680 Speaker 1: locking me in the basement until I did my math homework, 239 00:15:38,720 --> 00:15:40,640 Speaker 1: but you know, there was this push to get really 240 00:15:40,640 --> 00:15:44,760 Speaker 1: really good grades. And so I'm wondering now of having 241 00:15:44,800 --> 00:15:47,520 Speaker 1: a moment Kristen, like, I'm wondering now if you know, 242 00:15:47,600 --> 00:15:52,080 Speaker 1: maybe my grades were just in math, you know, like 243 00:15:52,120 --> 00:15:54,240 Speaker 1: I would have been a C student or something, but 244 00:15:54,360 --> 00:15:56,360 Speaker 1: I had to get that high B or that A. 245 00:15:58,000 --> 00:16:01,000 Speaker 1: Maybe I wasn't actually as bad at math, probably not, 246 00:16:01,800 --> 00:16:05,360 Speaker 1: probably not well. And we're going to get into that 247 00:16:05,600 --> 00:16:10,040 Speaker 1: issue of self assessment in talking about what is keeping 248 00:16:10,160 --> 00:16:12,760 Speaker 1: us from really closing up this math gender gap and 249 00:16:12,840 --> 00:16:17,800 Speaker 1: really debunking that stereotype that girls are bad at math, 250 00:16:17,800 --> 00:16:20,280 Speaker 1: that we're just naturally not as good as an at it, 251 00:16:20,560 --> 00:16:23,240 Speaker 1: that we're going to have to study above and beyond 252 00:16:23,840 --> 00:16:27,440 Speaker 1: in order to succeed. Right when we come back from 253 00:16:27,440 --> 00:16:30,600 Speaker 1: a quick break, so right before the break, we were 254 00:16:30,640 --> 00:16:35,320 Speaker 1: talking about confidence in math and self assessment, and we're 255 00:16:35,320 --> 00:16:38,040 Speaker 1: going to look at a few different aspects of what 256 00:16:38,280 --> 00:16:42,280 Speaker 1: keeps women and girls from progressing in their mathematical studies. 257 00:16:42,280 --> 00:16:44,000 Speaker 1: And a lot of this is coming from the Great 258 00:16:44,040 --> 00:16:48,400 Speaker 1: Report Why So Few Women in Science, Technology, Engineering, and Math, 259 00:16:48,760 --> 00:16:52,640 Speaker 1: which was created for the American Association of University Women. 260 00:16:52,760 --> 00:16:56,520 Speaker 1: And so one of these cultural factors limiting girls interest 261 00:16:56,560 --> 00:17:00,040 Speaker 1: in math is that whole self assessment issue, Like I 262 00:17:00,080 --> 00:17:01,920 Speaker 1: was just talking about, you know, maybe I was a 263 00:17:02,000 --> 00:17:04,639 Speaker 1: little bit better at math than I thought I was, 264 00:17:04,920 --> 00:17:08,320 Speaker 1: but because I needed that a I I thought I 265 00:17:08,400 --> 00:17:11,640 Speaker 1: kind of stunk at math. And so Kimberly Showman, who's 266 00:17:11,640 --> 00:17:14,840 Speaker 1: a U. C. Davis sociologist, found that girls whose math 267 00:17:14,920 --> 00:17:17,760 Speaker 1: test scores are at the top are less likely than 268 00:17:17,840 --> 00:17:21,560 Speaker 1: boys with average or good scores to pursue science and 269 00:17:21,640 --> 00:17:25,320 Speaker 1: engineering careers. She found that women are more cautious about 270 00:17:25,400 --> 00:17:28,399 Speaker 1: entering those fields unless they have very high scores to 271 00:17:28,440 --> 00:17:31,880 Speaker 1: begin with. Yeah, and this is a familiar theme throughout 272 00:17:31,920 --> 00:17:35,600 Speaker 1: all of these STEM episodes, um because on top of that, 273 00:17:35,760 --> 00:17:38,439 Speaker 1: not only is there that negative self assessment going on, 274 00:17:38,560 --> 00:17:43,040 Speaker 1: but also those cultural stereotypes of where men and women belong. 275 00:17:43,240 --> 00:17:49,080 Speaker 1: We think of your prototypical mathematician, he's probably a nerdy 276 00:17:49,240 --> 00:17:52,000 Speaker 1: ish looking guy in a tweet jacket and big glasses, 277 00:17:52,600 --> 00:17:56,040 Speaker 1: and people are more likely to associate math and science 278 00:17:56,040 --> 00:17:59,520 Speaker 1: with men than women. And on top of that, there's 279 00:17:59,560 --> 00:18:03,080 Speaker 1: that pen alt. They often hold negative opinions of women 280 00:18:03,240 --> 00:18:08,320 Speaker 1: in these more stereotypically masculine positions. Women are judged to 281 00:18:08,320 --> 00:18:13,240 Speaker 1: be less competent than men unless they are clearly successful. 282 00:18:13,800 --> 00:18:17,720 Speaker 1: But if she is she's considered less likable. Oh my god, 283 00:18:18,480 --> 00:18:20,840 Speaker 1: it's a it's an incredible catch twenty two that female 284 00:18:20,880 --> 00:18:24,920 Speaker 1: mathematicians find themselves in, and that seems like an unsolvable equation. 285 00:18:27,119 --> 00:18:29,280 Speaker 1: I would like to get more math puns in this 286 00:18:29,320 --> 00:18:33,040 Speaker 1: episode if possible. I'm just warning you, um, but when 287 00:18:33,080 --> 00:18:38,639 Speaker 1: we move through the math pipeline, as Steven Cecy and 288 00:18:38,680 --> 00:18:41,680 Speaker 1: Wendy Williams, who are a husband wife psychological science team 289 00:18:41,840 --> 00:18:44,840 Speaker 1: in two thousand ten, wanted to look at what was 290 00:18:44,880 --> 00:18:48,040 Speaker 1: going on with the drop off, because we talked about 291 00:18:48,080 --> 00:18:52,960 Speaker 1: how we have of women earning those math related bachelors, 292 00:18:53,040 --> 00:18:58,200 Speaker 1: but then pretty large drop earning the PhDs and even 293 00:18:58,240 --> 00:19:02,479 Speaker 1: fewer than that actually pursuing math related professions. And so 294 00:19:02,840 --> 00:19:07,000 Speaker 1: they wanted to dig through to see what was going on, 295 00:19:07,760 --> 00:19:11,399 Speaker 1: and they said, you know, it's not necessarily sex discrimination. 296 00:19:11,440 --> 00:19:14,359 Speaker 1: It's not necessarily that women are being penalized for not 297 00:19:14,440 --> 00:19:18,760 Speaker 1: being dislikable for maybe wanting to assume these more stereotypically 298 00:19:18,760 --> 00:19:22,520 Speaker 1: masculine roles, right because I mean they even point out 299 00:19:22,600 --> 00:19:25,199 Speaker 1: that women are slightly more likely than men to be 300 00:19:25,280 --> 00:19:28,760 Speaker 1: interviewed for and be offered tenure track jobs in math 301 00:19:28,800 --> 00:19:33,919 Speaker 1: related fields. Instead, women are choosing not to go into 302 00:19:34,000 --> 00:19:37,119 Speaker 1: math heavy fields or dropping out once they started. And 303 00:19:37,160 --> 00:19:40,560 Speaker 1: a lot of that has to do with not only 304 00:19:40,600 --> 00:19:43,199 Speaker 1: the roles that women play in society and that they 305 00:19:43,200 --> 00:19:47,000 Speaker 1: are expected to play in the family, but also just 306 00:19:47,040 --> 00:19:51,080 Speaker 1: sort of the dreams and aspirations that young girls have 307 00:19:51,560 --> 00:19:53,880 Speaker 1: um about what they want to be when they grow up, 308 00:19:53,920 --> 00:19:56,760 Speaker 1: whether it's math related or not. Yeah. One example they 309 00:19:56,800 --> 00:19:59,359 Speaker 1: gave was, rather than a ton of girls saying they 310 00:19:59,359 --> 00:20:02,359 Speaker 1: wanted to grow up in a physicist, they usually say 311 00:20:02,400 --> 00:20:04,720 Speaker 1: they would want to be something like a doctor or 312 00:20:04,960 --> 00:20:10,320 Speaker 1: a veterinarian, again echoing that engineering episode where women are 313 00:20:10,520 --> 00:20:14,639 Speaker 1: more likely than men to select a college major or 314 00:20:15,280 --> 00:20:20,320 Speaker 1: a career field that has some social aspects, some some 315 00:20:20,400 --> 00:20:25,760 Speaker 1: altruistic impact, which being a doctor, being a veterinarian, obviously 316 00:20:25,800 --> 00:20:31,239 Speaker 1: that might make more sense than being a physicist. Not 317 00:20:31,320 --> 00:20:33,480 Speaker 1: to say though, once you dig into physics and all 318 00:20:33,560 --> 00:20:36,760 Speaker 1: the real world impacts, obviously there's some cool things that 319 00:20:36,800 --> 00:20:39,800 Speaker 1: are happening there. But it's just not that the education 320 00:20:39,840 --> 00:20:42,320 Speaker 1: isn't there from the get go. We are informing girls 321 00:20:42,359 --> 00:20:45,800 Speaker 1: of these choices, right and CC and Williams also point 322 00:20:45,840 --> 00:20:48,959 Speaker 1: out that there is another aspect at work here. And 323 00:20:49,000 --> 00:20:52,120 Speaker 1: that is that among men and women with comparable math skills, 324 00:20:52,160 --> 00:20:55,639 Speaker 1: women are more likely to have outstanding verbal ability, so 325 00:20:55,760 --> 00:20:58,639 Speaker 1: maybe more doors are open to them if they want 326 00:20:58,680 --> 00:21:01,159 Speaker 1: to pursue slightly differ sort of a tweak on a 327 00:21:01,200 --> 00:21:03,919 Speaker 1: math job. And they said that guys in math have 328 00:21:04,040 --> 00:21:07,600 Speaker 1: fewer options basically accept to stick with math. But in 329 00:21:07,600 --> 00:21:10,000 Speaker 1: the cases where women do stick with math, if we're 330 00:21:10,080 --> 00:21:14,080 Speaker 1: looking more into academia, women are more likely to drop 331 00:21:14,119 --> 00:21:16,480 Speaker 1: out after they start a job as a professor, often 332 00:21:16,520 --> 00:21:21,840 Speaker 1: because of the childcare issue, because the workload required to 333 00:21:21,880 --> 00:21:26,320 Speaker 1: get tenure often does not mesh very well with the 334 00:21:26,400 --> 00:21:29,199 Speaker 1: workload of caring for a child, whereas a lot of 335 00:21:29,280 --> 00:21:32,760 Speaker 1: young male professors are more likely to have a stay 336 00:21:32,800 --> 00:21:35,439 Speaker 1: at home spouse. Yeah, the study author said that this 337 00:21:35,600 --> 00:21:38,640 Speaker 1: at the same time that a mathematician male or female, 338 00:21:38,720 --> 00:21:41,000 Speaker 1: is trying to do all of this work to pursue tenure. 339 00:21:41,400 --> 00:21:45,160 Speaker 1: That also tends to fall within the same years where 340 00:21:46,080 --> 00:21:48,840 Speaker 1: man or woman, you're you're gonna want to start getting married, 341 00:21:48,920 --> 00:21:53,040 Speaker 1: having kids, raising those kids, and so just because of 342 00:21:53,320 --> 00:21:57,359 Speaker 1: social breakdowns and social expectations, it seems that more women 343 00:21:57,440 --> 00:22:01,080 Speaker 1: in this field than men are giving up math to 344 00:22:01,440 --> 00:22:05,520 Speaker 1: pursue to dedicate themselves more to the family. And this 345 00:22:05,600 --> 00:22:08,800 Speaker 1: is the same kind of off ramp and quotes that 346 00:22:09,240 --> 00:22:12,440 Speaker 1: we see in pretty much any other profession where there 347 00:22:12,480 --> 00:22:13,959 Speaker 1: does seem to come a point that for a lot 348 00:22:14,000 --> 00:22:15,880 Speaker 1: of women, you hit that crossroads of am I going 349 00:22:15,920 --> 00:22:18,919 Speaker 1: to have a family and dial back or am I 350 00:22:19,000 --> 00:22:22,920 Speaker 1: going to forge onward with my career. But perhaps within 351 00:22:23,200 --> 00:22:29,159 Speaker 1: STEM and specifically talking about math, it's magnified because we 352 00:22:29,240 --> 00:22:34,159 Speaker 1: are still digging ourselves out of justice historical legacy of 353 00:22:34,160 --> 00:22:38,760 Speaker 1: it being a very male dominated realm, especially when we're 354 00:22:38,760 --> 00:22:43,440 Speaker 1: talking about the old guard of STEM academia, right and 355 00:22:43,440 --> 00:22:45,840 Speaker 1: and you know, going back to what I said about 356 00:22:46,040 --> 00:22:49,119 Speaker 1: women possibly dropping out of math and greater numbers to 357 00:22:49,320 --> 00:22:51,720 Speaker 1: dedicate themselves more to a family or to a career 358 00:22:51,760 --> 00:22:54,639 Speaker 1: that's not as demanding. I'm not in any way, you know, 359 00:22:54,720 --> 00:22:57,760 Speaker 1: placing blame on the women themselves. You know, as Williams 360 00:22:57,800 --> 00:23:00,560 Speaker 1: points out, universities can and should do a lot more 361 00:23:00,640 --> 00:23:03,480 Speaker 1: for a woman and for those men engaged in comparably 362 00:23:03,840 --> 00:23:07,840 Speaker 1: intensive caretaking, they more should be done in the field itself, 363 00:23:07,960 --> 00:23:13,640 Speaker 1: at the colleges and universities themselves to make this lifestyle, 364 00:23:13,800 --> 00:23:18,800 Speaker 1: this this career choice more um welcoming. Yeah, and there's 365 00:23:18,880 --> 00:23:25,240 Speaker 1: a generational impact of attracting more female math professors because 366 00:23:25,680 --> 00:23:31,000 Speaker 1: those women are going to serve as those visible reminders 367 00:23:31,040 --> 00:23:34,560 Speaker 1: to younger female students taking those early math classes that hey, 368 00:23:34,600 --> 00:23:37,560 Speaker 1: she's doing this, Hey I can do that. Maybe there's 369 00:23:37,600 --> 00:23:41,480 Speaker 1: more female female mentorship that can start happening. Um. That 370 00:23:41,560 --> 00:23:44,480 Speaker 1: visibility factor that we talked about again and again comes 371 00:23:44,560 --> 00:23:48,879 Speaker 1: up with this as well, and then rooting even farther 372 00:23:49,000 --> 00:23:55,520 Speaker 1: back from college into elementary school. It's so imperative that 373 00:23:55,600 --> 00:23:59,840 Speaker 1: we break through that stereotype and the stereotype threat that 374 00:24:00,119 --> 00:24:04,240 Speaker 1: girls are bad at math, right, because believing in the 375 00:24:04,280 --> 00:24:09,679 Speaker 1: potential for intellectual growth improves scores and outcomes. Basically believing 376 00:24:10,080 --> 00:24:12,560 Speaker 1: that it is not nature that leads us to be 377 00:24:12,720 --> 00:24:15,880 Speaker 1: good or bad at one thing or another. Yeah, it's 378 00:24:15,880 --> 00:24:20,280 Speaker 1: this whole issue of the growth mindset versus a fixed mindset, 379 00:24:20,320 --> 00:24:23,400 Speaker 1: And girls who believe that intelligence can expand with experience 380 00:24:23,480 --> 00:24:27,160 Speaker 1: and learning tend to do better on math tests specifically, 381 00:24:27,600 --> 00:24:30,119 Speaker 1: and they're more likely to say they want to continue 382 00:24:30,160 --> 00:24:33,640 Speaker 1: to study math in the future. Right, Because, as we've 383 00:24:33,680 --> 00:24:37,320 Speaker 1: talked about in our other STEM episodes, those negative stereotypes 384 00:24:37,359 --> 00:24:42,720 Speaker 1: about girls abilities lower their test performances. So thankfully there 385 00:24:42,720 --> 00:24:47,600 Speaker 1: are prominent mentors and role models out there from the classrooms. 386 00:24:47,600 --> 00:24:50,560 Speaker 1: We've had women already writing in to us saying I 387 00:24:50,840 --> 00:24:55,160 Speaker 1: teach math because I want to inspire and mentor other 388 00:24:55,200 --> 00:24:57,919 Speaker 1: girls to get involved with math, which is incredible, and 389 00:24:57,920 --> 00:24:59,720 Speaker 1: you can track that all the way up to higher 390 00:24:59,720 --> 00:25:03,639 Speaker 1: prof file examples of women like Danika mckeller, who played 391 00:25:03,760 --> 00:25:06,560 Speaker 1: Winnie on The Wonder Years, who wrote a book called 392 00:25:06,600 --> 00:25:09,879 Speaker 1: Math Doesn't Suck. And even though if you're not familiar 393 00:25:09,920 --> 00:25:11,600 Speaker 1: with the book, that might sound like, oh, she just 394 00:25:11,640 --> 00:25:14,080 Speaker 1: wrote a book, what's the big deal? But it actually 395 00:25:14,440 --> 00:25:16,440 Speaker 1: was met with a lot of critical acclaim And I 396 00:25:16,720 --> 00:25:19,520 Speaker 1: read an excerpt from it, and she does a great 397 00:25:19,600 --> 00:25:23,600 Speaker 1: job of explaining what turned her onto math and also 398 00:25:23,840 --> 00:25:27,840 Speaker 1: how math can have a direct impact on girls day 399 00:25:27,880 --> 00:25:30,960 Speaker 1: to day lives and why in so many ways math 400 00:25:31,040 --> 00:25:35,359 Speaker 1: can make your life better. Yeah, and other podcast shout 401 00:25:35,400 --> 00:25:38,520 Speaker 1: out she was on an episode of The Nerdest and 402 00:25:38,640 --> 00:25:43,159 Speaker 1: she sounds so awesome and intelligent and great and is 403 00:25:43,320 --> 00:25:46,680 Speaker 1: obviously such a great role model for young women. Um. 404 00:25:46,760 --> 00:25:50,320 Speaker 1: And then there's also initiatives like the Advantage Testing Foundations 405 00:25:50,359 --> 00:25:53,600 Speaker 1: Math Prize for girls. In two thousand thirteen, for instance, 406 00:25:54,000 --> 00:25:56,920 Speaker 1: two hundred and seventy six girls competed in the fifth 407 00:25:56,960 --> 00:26:00,200 Speaker 1: annual competition. So they're definitely girls out there who are 408 00:26:00,720 --> 00:26:05,159 Speaker 1: interested in math joining these competitions. Yeah, and I like 409 00:26:05,400 --> 00:26:07,840 Speaker 1: that there seemed to be there seems to be this 410 00:26:07,920 --> 00:26:12,680 Speaker 1: huge push from significant organizations to encourage girls in math um. 411 00:26:12,760 --> 00:26:15,800 Speaker 1: The Institute for Advanced Study, along with Princeton, hosts an 412 00:26:15,800 --> 00:26:19,880 Speaker 1: eleven day mentoring program for undergrad, grad and post doc 413 00:26:19,920 --> 00:26:23,239 Speaker 1: women in mathematics, so to try to close up that 414 00:26:23,280 --> 00:26:26,480 Speaker 1: pipeline cock all those cracks in it and keep them 415 00:26:26,520 --> 00:26:29,680 Speaker 1: in there. And also, just for another example, with higher education, 416 00:26:30,119 --> 00:26:32,520 Speaker 1: m i T has its Women in Math Conference to 417 00:26:32,600 --> 00:26:36,760 Speaker 1: celebrate students, alumni, and faculty contributions to mathematics. And there 418 00:26:36,760 --> 00:26:40,800 Speaker 1: are new grants now being offered for women at the 419 00:26:40,920 --> 00:26:44,840 Speaker 1: earlier phases of their math careers before we're getting into 420 00:26:44,880 --> 00:26:49,040 Speaker 1: a whole tenure track issue, just starting to incentivize women 421 00:26:49,080 --> 00:26:52,680 Speaker 1: to study and stick with math. And part of me 422 00:26:52,720 --> 00:26:55,400 Speaker 1: wishes that this episode of Math could have just been 423 00:26:56,000 --> 00:27:00,320 Speaker 1: highlighting all of the lives of female mathematicians like you 424 00:27:00,359 --> 00:27:02,840 Speaker 1: feel me Aloften Hayes. The first African American woman to 425 00:27:02,880 --> 00:27:05,399 Speaker 1: earn a PhD in math in the United States, just 426 00:27:05,480 --> 00:27:09,320 Speaker 1: to give as many examples as possible of Hey, no, 427 00:27:09,480 --> 00:27:11,479 Speaker 1: look look at what we've done, and we can do 428 00:27:11,560 --> 00:27:16,120 Speaker 1: so much more. But we have to at some point, 429 00:27:16,240 --> 00:27:22,280 Speaker 1: somehow culturally uproot that idea that the lady brain just 430 00:27:22,320 --> 00:27:25,919 Speaker 1: didn't cut out for all those numbers and things. Yeah, 431 00:27:26,160 --> 00:27:30,520 Speaker 1: I yeah, don't ever read the comments on the internet 432 00:27:31,080 --> 00:27:33,120 Speaker 1: when we were doing research for this. I mean, there 433 00:27:33,119 --> 00:27:35,919 Speaker 1: were some great articles out there about some incredible women 434 00:27:36,000 --> 00:27:39,920 Speaker 1: doing incredible things in the stem fields. But judging by 435 00:27:39,960 --> 00:27:43,520 Speaker 1: the comments there, I mean, there is that huge perception 436 00:27:43,600 --> 00:27:47,240 Speaker 1: that still exists. But people talk about these perceptions of 437 00:27:47,240 --> 00:27:50,960 Speaker 1: women as if they are hard and true facts, like well, 438 00:27:51,000 --> 00:27:55,840 Speaker 1: I mean women's brains just aren't wired for these things, right, Well, 439 00:27:55,880 --> 00:27:57,399 Speaker 1: I feel like you have to look at it on 440 00:27:57,440 --> 00:28:02,560 Speaker 1: an individual basis. I mean, different brains are drawn to 441 00:28:02,920 --> 00:28:06,760 Speaker 1: different types of things, which is good. It's good that 442 00:28:06,800 --> 00:28:09,560 Speaker 1: we're not all mathematicians walking around, and it's good that 443 00:28:09,600 --> 00:28:12,280 Speaker 1: we're not all poets walking around, you know what I mean. 444 00:28:12,880 --> 00:28:16,840 Speaker 1: I think it's important to remember that Bell curve and 445 00:28:16,880 --> 00:28:21,359 Speaker 1: how we're scattered around all of its many contours both 446 00:28:21,440 --> 00:28:25,840 Speaker 1: on the more math side and more on the verbal side, right, 447 00:28:25,880 --> 00:28:30,720 Speaker 1: And so I would encourage um all of our math 448 00:28:30,920 --> 00:28:34,119 Speaker 1: or STEM inclined listeners to go to our Facebook page 449 00:28:34,160 --> 00:28:37,960 Speaker 1: and write your stories. We've been getting some really incredible, inspiring, 450 00:28:38,160 --> 00:28:42,880 Speaker 1: encouraging stories from from women, and I think it would 451 00:28:42,920 --> 00:28:45,560 Speaker 1: be great if you guys just continued to post them 452 00:28:45,600 --> 00:28:48,760 Speaker 1: for other women to see. Yeah, and this is something 453 00:28:48,800 --> 00:28:51,760 Speaker 1: that we would like to continue in terms of spraying 454 00:28:51,800 --> 00:28:55,000 Speaker 1: the word helping out in terms of the visibility because 455 00:28:55,320 --> 00:29:00,160 Speaker 1: obviously Caroline and I are not women in stead him, 456 00:29:00,640 --> 00:29:05,560 Speaker 1: but we recognize the value of getting more women and 457 00:29:05,680 --> 00:29:08,880 Speaker 1: girls engaged and also promoting the work of women who 458 00:29:08,960 --> 00:29:12,320 Speaker 1: are doing incredible things already. And so this is not 459 00:29:12,360 --> 00:29:16,200 Speaker 1: an initiative that we want to stop just with this podcast. 460 00:29:16,720 --> 00:29:19,200 Speaker 1: So yeah, head over to our Facebook, tweet us at 461 00:29:19,200 --> 00:29:22,800 Speaker 1: mom Stuff podcast, send us your letters mom Stuff at 462 00:29:22,800 --> 00:29:25,560 Speaker 1: Discovery dot com. We really want to hear from you 463 00:29:25,960 --> 00:29:28,800 Speaker 1: and here what's really going on outside of all these 464 00:29:28,840 --> 00:29:31,600 Speaker 1: studies and statistics we've been tossing out in the past 465 00:29:31,640 --> 00:29:34,920 Speaker 1: four episodes to really learn what it's like out there 466 00:29:34,960 --> 00:29:38,280 Speaker 1: for women and STEM. So with that, we are going 467 00:29:38,320 --> 00:29:40,120 Speaker 1: to take a quick break and then get back to 468 00:29:40,240 --> 00:29:46,320 Speaker 1: a couple of letters. And now how about some letters. Well, 469 00:29:46,360 --> 00:29:50,680 Speaker 1: speaking of the actors we've been receiving from STEM Women Listening, 470 00:29:51,200 --> 00:29:54,080 Speaker 1: I have a letter here from Cassidy, who wrote, thank 471 00:29:54,120 --> 00:29:56,360 Speaker 1: you for your recent podcasts on women and science. I 472 00:29:56,400 --> 00:29:58,560 Speaker 1: wanted to drop you a line about an encounter I 473 00:29:58,600 --> 00:30:01,000 Speaker 1: had with my advisor and grad at school. I have 474 00:30:01,040 --> 00:30:04,280 Speaker 1: a PhD in biochemistry. A group of us have traveled 475 00:30:04,280 --> 00:30:07,280 Speaker 1: to see a play about Rosalind Franklin's discovery of the 476 00:30:07,280 --> 00:30:10,920 Speaker 1: structure of DNA and Watson and Crick's involvement. After the play, 477 00:30:11,000 --> 00:30:13,720 Speaker 1: we were talking about James Watson's views on women and 478 00:30:13,800 --> 00:30:17,960 Speaker 1: minorities and science, which are not favorable, and my advisor 479 00:30:18,000 --> 00:30:20,719 Speaker 1: at the time made a comment stating that, well, he 480 00:30:20,760 --> 00:30:22,920 Speaker 1: may have a point. I mean, sometimes the cells are 481 00:30:22,960 --> 00:30:25,880 Speaker 1: ready at two in the morning. I just remember staring 482 00:30:25,920 --> 00:30:28,560 Speaker 1: at him in horror and saying, well, if that's the case, 483 00:30:28,880 --> 00:30:31,440 Speaker 1: then I'll be there at two in the morning. I 484 00:30:31,600 --> 00:30:37,080 Speaker 1: just thought I would share, so thanks, Cassidy, PhD in biochemistry. 485 00:30:37,240 --> 00:30:40,120 Speaker 1: I just raised the roof for you a little bit. Cassidy. Okay, 486 00:30:40,120 --> 00:30:43,960 Speaker 1: I have a letter here from Emily Uh. She says, 487 00:30:44,840 --> 00:30:48,280 Speaker 1: while she was listening to our stem episode, she was 488 00:30:48,360 --> 00:30:52,520 Speaker 1: knuckle deep informal in preserving tissue samples for disease and 489 00:30:52,520 --> 00:30:57,560 Speaker 1: parasite analysis by history pathology m b D. A big deal, 490 00:30:57,640 --> 00:31:01,080 Speaker 1: A good deal, alright, so Emily right. I'm a lab 491 00:31:01,120 --> 00:31:04,640 Speaker 1: tech at a marine research lab that focuses on shellfish. 492 00:31:04,720 --> 00:31:06,840 Speaker 1: In my lab, and in many labs I have visited, 493 00:31:06,880 --> 00:31:09,600 Speaker 1: most of the actual work is done by female students 494 00:31:09,600 --> 00:31:13,400 Speaker 1: and technicians of varying levels of education BS to pH d, 495 00:31:13,920 --> 00:31:16,160 Speaker 1: while the higher up men are stuck in their offices 496 00:31:16,240 --> 00:31:19,680 Speaker 1: writing grand proposals. However, this seems to be changing, since 497 00:31:19,760 --> 00:31:23,560 Speaker 1: two of the three recently appointed professors or women. Otherwise, 498 00:31:23,600 --> 00:31:26,280 Speaker 1: nearly all of my fellow lab folk are women to 499 00:31:26,480 --> 00:31:29,880 Speaker 1: male to sixteen female. The classes and tours held at 500 00:31:29,880 --> 00:31:32,760 Speaker 1: our facility are typically dominated by female students, and it 501 00:31:32,800 --> 00:31:35,440 Speaker 1: feels like the majority of students that the scientific compasses 502 00:31:35,480 --> 00:31:38,400 Speaker 1: they've been to have been female. I think that the 503 00:31:38,480 --> 00:31:41,080 Speaker 1: gender gap will continue to close as people realize the 504 00:31:41,120 --> 00:31:44,360 Speaker 1: absurdity of the idea that there are functional differences in 505 00:31:44,400 --> 00:31:48,640 Speaker 1: the brains of men and women as applied to stem aptitude. 506 00:31:49,080 --> 00:31:51,240 Speaker 1: And she says, thanks for spreading the word and entertaining 507 00:31:51,240 --> 00:31:53,120 Speaker 1: me while you're at it. You got it, Emily, and 508 00:31:53,160 --> 00:31:56,560 Speaker 1: thank you for writing in, and you get that Shellfish 509 00:31:56,760 --> 00:32:00,320 Speaker 1: research and thanks to everybody who's written in. Mom Stuff 510 00:32:00,320 --> 00:32:02,600 Speaker 1: at discovery dot com is where you can send your letters. 511 00:32:02,760 --> 00:32:05,719 Speaker 1: Don't forget to follow us on Twitter at mom Stuff Podcast, 512 00:32:05,880 --> 00:32:08,640 Speaker 1: and you can also find us on Facebook messages. They're 513 00:32:08,720 --> 00:32:10,880 Speaker 1: like us while you're at it, and you can also 514 00:32:11,640 --> 00:32:14,120 Speaker 1: like plenty of stuff over on our Instagram where it's 515 00:32:14,160 --> 00:32:16,560 Speaker 1: stuff Mom Never told You, and also on Tumbler where 516 00:32:16,560 --> 00:32:20,200 Speaker 1: stuff Mom Never told You dot tumbler dot com. 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