1 00:00:00,320 --> 00:00:03,000 Speaker 1: Brought to you by the Reinvented two thousand twelve Camray. 2 00:00:03,240 --> 00:00:10,000 Speaker 1: It's ready. Are you welcome to stuff Mom never told you? 3 00:00:10,200 --> 00:00:17,560 Speaker 1: From house Stop works dot com. Hello, and welcome to 4 00:00:17,600 --> 00:00:21,760 Speaker 1: the podcast. I'm Kristen and I'm Caroline. Today Caroline and 5 00:00:21,800 --> 00:00:24,360 Speaker 1: I are going to talk about something that many listeners 6 00:00:24,400 --> 00:00:29,320 Speaker 1: have requested for a while. Just pops up women in science, 7 00:00:29,600 --> 00:00:32,840 Speaker 1: science science, How they blinded us? Get it? I wish 8 00:00:32,840 --> 00:00:34,960 Speaker 1: we were wearing lab coats for this episode out of 9 00:00:35,000 --> 00:00:40,120 Speaker 1: the listeners, No, we're not true. Imagine that, listens, we 10 00:00:40,240 --> 00:00:43,280 Speaker 1: are in lab coats with beakers on the table because 11 00:00:43,320 --> 00:00:47,800 Speaker 1: we're gonna talk about science today. Because women in science, 12 00:00:48,240 --> 00:00:53,520 Speaker 1: although they are getting more attention these days, still there 13 00:00:53,680 --> 00:00:58,480 Speaker 1: is a gap, right And actually the Independent Women's Forum, 14 00:00:58,840 --> 00:01:02,600 Speaker 1: a research and education institution, blames it on innate differences 15 00:01:02,640 --> 00:01:07,000 Speaker 1: and aptitudes, temperament and interests between men and women. Basically, 16 00:01:07,680 --> 00:01:09,600 Speaker 1: it kind of sounds like the stereotype of like, well, 17 00:01:09,640 --> 00:01:14,160 Speaker 1: women just starting good at science and um. But yeah, 18 00:01:14,200 --> 00:01:21,080 Speaker 1: they claim that the gender gap in STEM, which is science, technology, engineering, 19 00:01:21,160 --> 00:01:26,520 Speaker 1: and math madics mathematics STEM courses, UM I have to 20 00:01:26,600 --> 00:01:29,640 Speaker 1: do with these brain differences. And one of the very first. 21 00:01:29,680 --> 00:01:32,680 Speaker 1: I think it might be. The very first episode of 22 00:01:32,680 --> 00:01:35,280 Speaker 1: Stuff Mom Never Told You was on whether or not 23 00:01:35,360 --> 00:01:38,080 Speaker 1: men and women have different brains. So we can go 24 00:01:38,120 --> 00:01:42,319 Speaker 1: back and listen to that if you like after this podcast. Um, 25 00:01:42,360 --> 00:01:47,200 Speaker 1: but at the same time, women are are good at 26 00:01:47,240 --> 00:01:50,440 Speaker 1: science and math. Yeah. Well, uh, you know, in all 27 00:01:50,480 --> 00:01:53,800 Speaker 1: this reading we did for this podcast, a lot of 28 00:01:53,800 --> 00:01:56,360 Speaker 1: the sources say that the numbers of men and women 29 00:01:56,720 --> 00:01:59,320 Speaker 1: interested in science and pursuing science in high school and 30 00:01:59,360 --> 00:02:02,040 Speaker 1: college about the same. It's there's not a huge difference. 31 00:02:02,080 --> 00:02:06,320 Speaker 1: It's it's not until after graduation, when maybe it's time 32 00:02:06,360 --> 00:02:08,919 Speaker 1: to pursue a career, take your career a little further, 33 00:02:09,040 --> 00:02:11,440 Speaker 1: that the numbers start to skew a little. Right. Um, 34 00:02:11,440 --> 00:02:14,480 Speaker 1: this is coming from the National Science Foundation in two 35 00:02:14,480 --> 00:02:18,359 Speaker 1: thousand six, fifty percent of students who enroll and graduate 36 00:02:18,400 --> 00:02:22,480 Speaker 1: courses in science or engineering are women. So obviously, while 37 00:02:22,520 --> 00:02:26,040 Speaker 1: our brains might work a little bit differently, obviously women 38 00:02:26,160 --> 00:02:30,480 Speaker 1: do just fine in STEM courses. But like you said, Caroline, 39 00:02:30,520 --> 00:02:33,480 Speaker 1: once they get into the real world, those numbers start 40 00:02:33,520 --> 00:02:36,800 Speaker 1: to drop. Because between two thousand and two thousand five, 41 00:02:37,240 --> 00:02:41,880 Speaker 1: only of women were employed out of school and employed 42 00:02:42,320 --> 00:02:46,320 Speaker 1: in the computer field. A leading employer of science and 43 00:02:46,360 --> 00:02:50,440 Speaker 1: engineering grads. Yeah, and numbers, numbers have jumped, they've they've 44 00:02:50,480 --> 00:02:53,920 Speaker 1: gotten better for sure. Um one percent I found was 45 00:02:54,040 --> 00:02:56,960 Speaker 1: that the percent of doctoral degrees awarded to women in 46 00:02:57,000 --> 00:03:01,840 Speaker 1: engineering uh IN was one percent, and in two thousand 47 00:03:01,880 --> 00:03:05,280 Speaker 1: one that jumped to eight So I feel like more 48 00:03:05,360 --> 00:03:09,440 Speaker 1: women are, you know, maybe sticking with it in school 49 00:03:09,440 --> 00:03:12,400 Speaker 1: at least. And if women stick with it, it will 50 00:03:12,680 --> 00:03:16,160 Speaker 1: pay off. Literally. This is a newer from a new 51 00:03:16,200 --> 00:03:18,800 Speaker 1: report came out in August from the U S. Commerce 52 00:03:18,840 --> 00:03:21,880 Speaker 1: Department that's been it's as part of a series of 53 00:03:21,919 --> 00:03:25,480 Speaker 1: reports actually that they've been doing on women and STEM 54 00:03:25,520 --> 00:03:29,440 Speaker 1: exploring why this gender gap exists. Because they want more. 55 00:03:29,720 --> 00:03:33,280 Speaker 1: The government wants more women to pursue STEM courses not 56 00:03:33,360 --> 00:03:37,960 Speaker 1: only in college but also beyond, like somehow closing off 57 00:03:38,000 --> 00:03:41,920 Speaker 1: that that exit ramp that seems to be happening. And 58 00:03:42,280 --> 00:03:46,200 Speaker 1: science pays because women in STEM jobs right now earned 59 00:03:46,240 --> 00:03:50,080 Speaker 1: thirty more than women in non STEM jobs. I wish 60 00:03:50,080 --> 00:03:55,040 Speaker 1: physics had made sense, I know, why aren't we a 61 00:03:55,680 --> 00:03:59,920 Speaker 1: And in addition, the gender pay gap in STEM industries 62 00:04:00,880 --> 00:04:04,520 Speaker 1: narrower than than in non stem sounds good to me. Yeah, 63 00:04:04,600 --> 00:04:08,360 Speaker 1: so the money's there, although the Gender Wage EPI still exists. 64 00:04:11,760 --> 00:04:15,280 Speaker 1: But the point of this episode today is talk about 65 00:04:15,600 --> 00:04:19,560 Speaker 1: women in science because they exist, and lack of role 66 00:04:19,600 --> 00:04:24,200 Speaker 1: models is one reason that some people offer for the 67 00:04:24,360 --> 00:04:28,599 Speaker 1: STEM gender gap. But we're gonna we're gonna offer out 68 00:04:28,680 --> 00:04:32,640 Speaker 1: some offer up some role models today and not once 69 00:04:32,720 --> 00:04:35,800 Speaker 1: that you may have heard of before. Right. Well, although 70 00:04:35,880 --> 00:04:38,920 Speaker 1: let's start with what you heard of. There are some 71 00:04:38,920 --> 00:04:40,760 Speaker 1: good ones out there that the people have heard of. 72 00:04:40,839 --> 00:04:44,000 Speaker 1: There's Murray Currey, yes, who she had a pretty steady 73 00:04:44,120 --> 00:04:46,920 Speaker 1: rise up the science latter she has pretty much always 74 00:04:46,960 --> 00:04:49,919 Speaker 1: been amazing. Yeah, and she won the Nobel Prize in 75 00:04:49,920 --> 00:04:53,240 Speaker 1: physics in nineteen o three, and then for a second 76 00:04:53,240 --> 00:04:58,800 Speaker 1: time she want a Nobel prizing chemistry in nineteen eleven. There, yeah, Grandma. Um, 77 00:04:58,839 --> 00:05:00,840 Speaker 1: I do have a book recommendation and two. It's one 78 00:05:00,880 --> 00:05:02,360 Speaker 1: that I have not read yet, but I've heard an 79 00:05:02,360 --> 00:05:04,960 Speaker 1: interview with the author and it sounds fascinating. It's called 80 00:05:05,200 --> 00:05:09,160 Speaker 1: Radioactive Marie and Pierre Curie, A Tale of Love and 81 00:05:09,240 --> 00:05:13,040 Speaker 1: Fallout by Lauren Redness. Good title, I know, maybe that'll 82 00:05:13,080 --> 00:05:17,159 Speaker 1: be my fall reading. Um. I wanted to mention Ada 83 00:05:17,200 --> 00:05:21,839 Speaker 1: Byron known as Lady Lovelace, and also daughter of poet 84 00:05:21,920 --> 00:05:27,280 Speaker 1: Lord Byron. She in eighteen forty three developed what is 85 00:05:27,360 --> 00:05:32,920 Speaker 1: regarded as the first ever computer program. Yes, well there 86 00:05:33,000 --> 00:05:35,400 Speaker 1: is there's another scientist more recent who I just want 87 00:05:35,400 --> 00:05:38,240 Speaker 1: to hug. I've always loved this woman, Jane Goodall. Yes, 88 00:05:38,320 --> 00:05:40,360 Speaker 1: we've all, we've all heard about her in the Chimpanzee 89 00:05:40,400 --> 00:05:42,479 Speaker 1: She went to Tanzania as a twenty three year old 90 00:05:43,000 --> 00:05:47,919 Speaker 1: to observe how champion chimpanzees interacted. Basically, she took along 91 00:05:47,960 --> 00:05:50,280 Speaker 1: a notebook and binoculars and she managed to make all 92 00:05:50,279 --> 00:05:52,880 Speaker 1: these incredible discoveries. And then, of course we've got to 93 00:05:52,960 --> 00:05:55,640 Speaker 1: mention Sally Ride, who we talked about in our episode 94 00:05:55,720 --> 00:05:59,960 Speaker 1: on astronaut first woman in Space. There's American woman, American woman, 95 00:06:00,000 --> 00:06:05,040 Speaker 1: a woman, that's right, I'm sorry, no harm um And 96 00:06:05,160 --> 00:06:08,800 Speaker 1: she is a physics professor at the University of California, 97 00:06:08,800 --> 00:06:14,760 Speaker 1: San Diego and then became director of California's Space Institute. Yeah, 98 00:06:14,880 --> 00:06:18,240 Speaker 1: she's written children's books about science, get the kids interested, 99 00:06:18,800 --> 00:06:22,640 Speaker 1: and her um company, Sally Ride Science, seeks to motivate 100 00:06:22,680 --> 00:06:25,400 Speaker 1: girls to pursue careers in stimfield. Yeah, so she would 101 00:06:25,480 --> 00:06:27,919 Speaker 1: enjoy this episode. She would Sally Ride would be on 102 00:06:27,960 --> 00:06:31,960 Speaker 1: board with what we're talking about. Sally, Hey, you're out there. Um. 103 00:06:32,000 --> 00:06:34,640 Speaker 1: But like I said, the point of today's podcast is 104 00:06:34,720 --> 00:06:38,800 Speaker 1: to offer all some names that listeners might not have 105 00:06:38,839 --> 00:06:42,200 Speaker 1: heard of before, right, some women who changed our world, Yes, 106 00:06:42,320 --> 00:06:44,440 Speaker 1: affected science for the better, but you know, maybe you 107 00:06:44,480 --> 00:06:48,120 Speaker 1: don't know their names. There's Rosalind Elsie Franklin. She's a 108 00:06:48,160 --> 00:06:51,760 Speaker 1: molecular biologist. She started out at an all girls school 109 00:06:51,760 --> 00:06:54,200 Speaker 1: in London, one of the few to actually offer physics 110 00:06:54,240 --> 00:06:56,839 Speaker 1: and chemistry courses. Yeah. And the reason why you might 111 00:06:56,880 --> 00:07:01,279 Speaker 1: not have heard of Rosalind Franklin's name is because two 112 00:07:01,320 --> 00:07:04,360 Speaker 1: guys by the name of James D. Watson and Francis 113 00:07:04,480 --> 00:07:08,520 Speaker 1: Crick stole her thunder absolutely beat her to the punch. 114 00:07:08,640 --> 00:07:12,120 Speaker 1: She did all this work on X ray diffraction techniques. 115 00:07:12,480 --> 00:07:14,200 Speaker 1: So she worked in Paris for a while and after 116 00:07:14,240 --> 00:07:17,120 Speaker 1: returning to England in nineteen fifty one, she worked at 117 00:07:17,120 --> 00:07:21,680 Speaker 1: a lab where she encountered Maurice Wilkins. Fortunately, Morris Wilkins 118 00:07:21,680 --> 00:07:24,240 Speaker 1: thought she was a an assistant in the lab and 119 00:07:24,480 --> 00:07:27,119 Speaker 1: she was a woman. Yeah, naturally, you know she's carrying 120 00:07:27,160 --> 00:07:28,800 Speaker 1: Martiniz around or something. I don't know. I don't know 121 00:07:28,840 --> 00:07:32,720 Speaker 1: why Anyway, he found her X ray images and showed 122 00:07:32,720 --> 00:07:36,360 Speaker 1: them to James Watson and Francis Crick. Yes, because Wilkins, 123 00:07:37,120 --> 00:07:42,040 Speaker 1: Crick and Watson, we're all working on unraveling the structure 124 00:07:42,280 --> 00:07:47,960 Speaker 1: of DNA, and Rosalind Franklin had taken these amazing X 125 00:07:48,080 --> 00:07:51,600 Speaker 1: ray photographs of d N A quote, the most beautiful 126 00:07:51,800 --> 00:07:57,080 Speaker 1: X ray photographs of any substance ever taken, one scientist quoted. 127 00:07:57,520 --> 00:08:02,560 Speaker 1: And so they showed, showed the guy sees incredible images 128 00:08:02,600 --> 00:08:07,760 Speaker 1: of DNA, and then they won the Nobel Prize in 129 00:08:07,840 --> 00:08:11,360 Speaker 1: nineteen sixty two, thanks guys, and she um well. One 130 00:08:11,360 --> 00:08:13,360 Speaker 1: of the reasons too, they think that that her work 131 00:08:13,360 --> 00:08:17,720 Speaker 1: has not been more widely recognized as such a major 132 00:08:17,800 --> 00:08:22,200 Speaker 1: contribution to understanding the structure of DNA because soon after 133 00:08:22,280 --> 00:08:24,760 Speaker 1: all of that went down, she got really sick. Yeah, 134 00:08:24,760 --> 00:08:27,520 Speaker 1: she got a vary in cancer and died at thirty seven, right, 135 00:08:27,640 --> 00:08:31,960 Speaker 1: very young, very young. Uh So Rosalind Franklin slewly, but 136 00:08:32,000 --> 00:08:35,040 Speaker 1: surely I think there's been more more spotlight on her 137 00:08:35,080 --> 00:08:38,439 Speaker 1: on her work. But definitely a female scientist who deserves 138 00:08:38,559 --> 00:08:41,320 Speaker 1: a lot of credit. Uh. And then we have Dorothy 139 00:08:41,559 --> 00:08:48,360 Speaker 1: Crowfoot Hodgkin, the founder of protein crystallography. Yeah, Basically, crystallography 140 00:08:48,400 --> 00:08:51,559 Speaker 1: is X rays that can determine the arrangement of atoms. 141 00:08:51,920 --> 00:08:54,880 Speaker 1: And she and her mentor applied this X ray diffraction 142 00:08:54,960 --> 00:08:57,760 Speaker 1: to crystals of biological substances. And I want to say 143 00:08:57,800 --> 00:09:01,439 Speaker 1: that her mentor J. D. Bernal is the person who said, uh, 144 00:09:01,520 --> 00:09:05,480 Speaker 1: who referred to Rosalind Franklin's X ray photography is the 145 00:09:05,480 --> 00:09:08,079 Speaker 1: most most beautiful of DNA. Get to see people supporting 146 00:09:08,080 --> 00:09:10,760 Speaker 1: women in science exactly, Yeah, because a lot of these 147 00:09:10,760 --> 00:09:13,480 Speaker 1: women as they're as they're growing up in pursuing these 148 00:09:13,480 --> 00:09:17,560 Speaker 1: scientific fields are either discouraged by their parents, especially their fathers, 149 00:09:18,000 --> 00:09:23,880 Speaker 1: are discouraged by male colleagues, or aren't paid for their work. Right, 150 00:09:23,960 --> 00:09:25,679 Speaker 1: Like one woman will talk about in a little bit 151 00:09:26,120 --> 00:09:29,200 Speaker 1: was never paid for most of her work and yet 152 00:09:29,280 --> 00:09:33,840 Speaker 1: became a Nobel Nobel Prize winner. But back to Dorothy 153 00:09:33,920 --> 00:09:39,080 Speaker 1: Crowfoot hodgkin Um. Her contributions included solutions to structures of 154 00:09:39,160 --> 00:09:42,600 Speaker 1: things by being able to see what they look like 155 00:09:42,760 --> 00:09:47,640 Speaker 1: using the chrystallography, so seeing the structure of things like cholesterol, penicillin, 156 00:09:47,760 --> 00:09:50,920 Speaker 1: which earned her an election as a Fellow of the 157 00:09:51,000 --> 00:09:54,040 Speaker 1: Royal Society in nineteen forty seven, and she won her 158 00:09:54,080 --> 00:09:57,960 Speaker 1: Nobel Prize in chemistry in ninety seven for seeing the 159 00:09:57,960 --> 00:10:01,240 Speaker 1: structure of or I guess unraveling the structure of vitamin 160 00:10:01,280 --> 00:10:03,880 Speaker 1: B twelve right. And then there was insulin. That was 161 00:10:03,920 --> 00:10:06,840 Speaker 1: her major project. Um she worked on it for thirty 162 00:10:06,920 --> 00:10:09,959 Speaker 1: four years, actually found the insulent structure in nineteen fifty 163 00:10:10,080 --> 00:10:14,040 Speaker 1: nine and went back and reevaluated in with the help 164 00:10:14,200 --> 00:10:18,240 Speaker 1: of computers. And it makes sense that Dorothy Crowfoot Hodgkin 165 00:10:18,320 --> 00:10:21,360 Speaker 1: I really like her whole name. That's saying that that 166 00:10:21,520 --> 00:10:24,960 Speaker 1: Dorothy was such a genius because she came from from 167 00:10:25,080 --> 00:10:28,079 Speaker 1: quite a family. Her dad was an archaeologist in Egypt, 168 00:10:28,280 --> 00:10:30,720 Speaker 1: and her mom was an expert on coptic tiles, and 169 00:10:30,760 --> 00:10:33,480 Speaker 1: she married an expert in African study. I would absolutely 170 00:10:33,520 --> 00:10:35,440 Speaker 1: go to their dinner party. I wouldn't be able to 171 00:10:35,480 --> 00:10:37,079 Speaker 1: keep up with anything they said, but I would just 172 00:10:37,120 --> 00:10:39,880 Speaker 1: sit there with my mouth wide openly sitting nod with 173 00:10:39,920 --> 00:10:42,120 Speaker 1: that with a smile on your face. Uh. And then 174 00:10:42,200 --> 00:10:44,240 Speaker 1: at the age of eighty, she finally got a visa 175 00:10:44,320 --> 00:10:46,800 Speaker 1: and traveled to the US giving talks to standing room 176 00:10:46,840 --> 00:10:51,040 Speaker 1: only crowds about insulin crystallography in its future. So these 177 00:10:51,080 --> 00:10:56,319 Speaker 1: women still working in in old age. But next up 178 00:10:56,640 --> 00:10:59,760 Speaker 1: is perhaps one of those underappreciated or at least under 179 00:10:59,760 --> 00:11:05,800 Speaker 1: pay scientists that we talked about today, Maria Geppert Mayor. Yeah, 180 00:11:05,840 --> 00:11:09,360 Speaker 1: she came. She also came from a very educated family. 181 00:11:09,440 --> 00:11:11,240 Speaker 1: And and you know, as opposed to some of the 182 00:11:11,240 --> 00:11:14,280 Speaker 1: other people we've talked about, you know, their fathers discouraged them, 183 00:11:14,600 --> 00:11:16,840 Speaker 1: didn't think a woman should go out and get an education, 184 00:11:16,920 --> 00:11:20,920 Speaker 1: especially in science. It was actually sort of understood that 185 00:11:21,000 --> 00:11:24,200 Speaker 1: she would go to university. Um. She actually her social 186 00:11:24,200 --> 00:11:29,280 Speaker 1: circle included physicist Neil's Bore and Max Born. And she 187 00:11:29,320 --> 00:11:31,640 Speaker 1: started out actually as a mathematician in school, but turned 188 00:11:31,640 --> 00:11:35,320 Speaker 1: her focus to physics. So somehow, to me hearing that, 189 00:11:35,440 --> 00:11:38,280 Speaker 1: it seems like somehow her brain got even smarter. Yeah, 190 00:11:38,320 --> 00:11:40,760 Speaker 1: I know, because whoa I can do I can do math. 191 00:11:40,800 --> 00:11:43,600 Speaker 1: I was fine with math in school, but physics and 192 00:11:43,640 --> 00:11:48,040 Speaker 1: I did not get along at all. Maria Geppert Mayor, 193 00:11:48,120 --> 00:11:52,440 Speaker 1: you on a genius um. But listen to this story. 194 00:11:52,760 --> 00:11:55,600 Speaker 1: So she comes to the US with her husband, also 195 00:11:55,720 --> 00:12:00,520 Speaker 1: a physicist, and continues her research even though she's not 196 00:12:00,559 --> 00:12:04,000 Speaker 1: being paid for it, right, and while quote unquote you know, unemployed, 197 00:12:04,440 --> 00:12:07,319 Speaker 1: she went on to produce tin papers and a textbook 198 00:12:07,360 --> 00:12:10,240 Speaker 1: and a physics textbook. Yeah. I mean, you know, I 199 00:12:10,240 --> 00:12:11,920 Speaker 1: guess if I was unemployed, I could do a lot 200 00:12:11,920 --> 00:12:14,920 Speaker 1: of cool things, do not anything to do with physics. 201 00:12:14,960 --> 00:12:17,920 Speaker 1: But still, but she was working with her husband in 202 00:12:18,000 --> 00:12:22,480 Speaker 1: collaborating with another physics professor, became a chemical physics and 203 00:12:22,520 --> 00:12:25,480 Speaker 1: then figured out, oh, I don't know the color of 204 00:12:25,600 --> 00:12:29,640 Speaker 1: organic molecules. Sure, why not? She went on. They moved 205 00:12:29,679 --> 00:12:32,440 Speaker 1: to Chicago, and I think she was more welcome there. 206 00:12:32,440 --> 00:12:34,600 Speaker 1: She actually, I think got a paycheck at some point 207 00:12:34,640 --> 00:12:38,439 Speaker 1: in Chicago. She worked at she was a professor in 208 00:12:38,480 --> 00:12:41,440 Speaker 1: the physics department and the Institute for Nuclear Studies, and 209 00:12:41,480 --> 00:12:44,560 Speaker 1: also worked at the Argon National Laboratory. Yeah, but she 210 00:12:44,600 --> 00:12:47,640 Speaker 1: did not secure full time, paid work in her field 211 00:12:47,920 --> 00:12:50,360 Speaker 1: until she was fifty three years Yeah. Can you imagine 212 00:12:50,360 --> 00:12:52,040 Speaker 1: at that time, I'm like wine and down. I'm ready 213 00:12:52,080 --> 00:12:54,160 Speaker 1: to go live on a yacht. I'm where, you know, 214 00:12:54,720 --> 00:13:01,040 Speaker 1: retire with my pat you'll definitely uh um. But then 215 00:13:01,400 --> 00:13:05,360 Speaker 1: she won the Nobel Prize in nineteen sixty three, and 216 00:13:05,600 --> 00:13:09,319 Speaker 1: the headline in the San Diego paper read s, d 217 00:13:09,679 --> 00:13:13,160 Speaker 1: San Diego mother wins the Nobel Prize. I mean, really, 218 00:13:13,520 --> 00:13:15,600 Speaker 1: even still we can't just give her a little bit 219 00:13:15,640 --> 00:13:18,200 Speaker 1: of credit for actually being a scientist and around right. Um, 220 00:13:18,240 --> 00:13:23,240 Speaker 1: but she developed a nuclear shell model of atomic nuclei, 221 00:13:23,520 --> 00:13:27,760 Speaker 1: so that essentially figured us out what what atomic nuclei 222 00:13:27,920 --> 00:13:30,760 Speaker 1: the outside of them looks like. It's there's nothing to 223 00:13:30,880 --> 00:13:34,200 Speaker 1: do with magic numbers, which I could not fully explain 224 00:13:34,280 --> 00:13:36,640 Speaker 1: to you listeners if you have any clust to what 225 00:13:36,679 --> 00:13:40,120 Speaker 1: it is that those represent the numbers of protons and 226 00:13:41,080 --> 00:13:45,560 Speaker 1: arranged in shells in the atoms nuclear Thanks Kristen. That's 227 00:13:45,600 --> 00:13:49,440 Speaker 1: not from my notes, No, not at all. How you 228 00:13:49,480 --> 00:13:54,319 Speaker 1: have a broad knowledge of things. Um, moving on. I 229 00:13:54,320 --> 00:13:56,240 Speaker 1: I like to save the best for last, not not 230 00:13:56,320 --> 00:14:00,280 Speaker 1: that all these women. I've been so excited about this woman. Okay, 231 00:14:00,320 --> 00:14:02,520 Speaker 1: I want you after the podcast, after you listen to this, 232 00:14:02,960 --> 00:14:05,000 Speaker 1: or while you're listening, you know, if you're on your 233 00:14:05,000 --> 00:14:11,240 Speaker 1: computer anyway, google Rita Levi montal Cini. She's still a lot. 234 00:14:11,360 --> 00:14:14,720 Speaker 1: She is a brain scientist who also happens to be 235 00:14:14,760 --> 00:14:18,319 Speaker 1: the oldest living Nobel laureate. The woman. Okay, have you 236 00:14:18,320 --> 00:14:20,520 Speaker 1: you know Ghostbusters when they go to the basement of 237 00:14:20,560 --> 00:14:24,120 Speaker 1: the library and there's that, um, there's that ghost, that 238 00:14:24,200 --> 00:14:28,520 Speaker 1: librarian ghost that's what she dresses like she looks so 239 00:14:28,920 --> 00:14:31,680 Speaker 1: she's a very spiffy. Well, she's very spell well, let 240 00:14:31,760 --> 00:14:35,200 Speaker 1: listeners google image here, because she really does look incredible. 241 00:14:35,240 --> 00:14:38,160 Speaker 1: She has an amazing quaff of shockingly w Yeah, and 242 00:14:38,160 --> 00:14:41,480 Speaker 1: this this woman, she's incredible. She her work with Stanley 243 00:14:41,520 --> 00:14:44,520 Speaker 1: Cohen led to a breakthrough in neurological science for the 244 00:14:44,560 --> 00:14:47,840 Speaker 1: discovery of nerve growth factor, a discovery that she says 245 00:14:47,920 --> 00:14:50,720 Speaker 1: is the highlight of her life. A nerve growth growth 246 00:14:50,760 --> 00:14:55,360 Speaker 1: factor is a protein that basically stimulates neural development um 247 00:14:55,480 --> 00:15:00,920 Speaker 1: and promotes nerve cell growth. Right. Um. She won the 248 00:15:00,920 --> 00:15:04,080 Speaker 1: Nobel Prize in Physiology and Medicine in nineteen eighty six 249 00:15:04,120 --> 00:15:07,120 Speaker 1: at the age of seventy seven. She's no slouchy and 250 00:15:07,320 --> 00:15:09,040 Speaker 1: she was not living on a yacht. How old does 251 00:15:09,080 --> 00:15:13,040 Speaker 1: she know? D and two and two? She's still going. Yes, 252 00:15:13,160 --> 00:15:17,640 Speaker 1: she founded the European Brain Research Institute and still goes 253 00:15:17,680 --> 00:15:20,160 Speaker 1: to work every day. Yeah, she still goes. She encourages 254 00:15:20,200 --> 00:15:22,960 Speaker 1: our students and while she admitted that yes, she does 255 00:15:23,040 --> 00:15:26,200 Speaker 1: encourage a lot of the female science students, she doesn't 256 00:15:26,240 --> 00:15:29,040 Speaker 1: really see a difference between men and women, especially in 257 00:15:29,160 --> 00:15:33,400 Speaker 1: their brains. Obviously, her her expertise is in neurology, and 258 00:15:33,640 --> 00:15:35,760 Speaker 1: she was quoted in the Times of London saying that 259 00:15:36,360 --> 00:15:39,920 Speaker 1: she really sees absolutely no difference between male and female 260 00:15:39,960 --> 00:15:45,320 Speaker 1: brains and attributes a lot of our behavior to environment 261 00:15:45,360 --> 00:15:49,640 Speaker 1: and again it's it's nature and nurtures. Well. In the Times, 262 00:15:49,680 --> 00:15:52,000 Speaker 1: she also is quoted as saying that we're all doomed 263 00:15:53,200 --> 00:15:55,840 Speaker 1: because in all of her years of brain research. You know, 264 00:15:55,920 --> 00:15:58,440 Speaker 1: she talks about the two hemispheres of the brain. One 265 00:15:58,560 --> 00:16:02,400 Speaker 1: quote unquote ancient rules our emotions and instincts. The other 266 00:16:02,440 --> 00:16:04,800 Speaker 1: one is more modern, where we use rational thoughts to 267 00:16:04,920 --> 00:16:07,880 Speaker 1: figure out our problems. And she says that the world's 268 00:16:07,920 --> 00:16:13,080 Speaker 1: problems today, terrorism, fundamentalism, et cetera, can be blamed on 269 00:16:13,520 --> 00:16:18,800 Speaker 1: people using their ancient, emotional instinctual brain too often. So 270 00:16:18,880 --> 00:16:24,240 Speaker 1: you've become too emotionally driven and are driving ourselves crazy. Yeah, 271 00:16:24,320 --> 00:16:26,640 Speaker 1: listen to this quote. She says, it was the part 272 00:16:26,640 --> 00:16:29,160 Speaker 1: of our brains which got us down from trees. But 273 00:16:29,240 --> 00:16:32,280 Speaker 1: it is the cause of all the disasters and the 274 00:16:32,360 --> 00:16:34,760 Speaker 1: cause of great danger to our planet today. It is 275 00:16:34,800 --> 00:16:38,720 Speaker 1: taking the human race towards extinction. The end is already 276 00:16:38,760 --> 00:16:41,920 Speaker 1: at hand. Oh yeah, that sounds a little scary. I 277 00:16:41,960 --> 00:16:45,680 Speaker 1: know it's freaking me out. But she I also like 278 00:16:45,840 --> 00:16:50,640 Speaker 1: her key to longevity, which is really never sleeping and 279 00:16:50,680 --> 00:16:53,240 Speaker 1: only eating one meal a day. She eats, she eats lunch, 280 00:16:53,480 --> 00:16:55,400 Speaker 1: and she says maybe an orange or a bowl of 281 00:16:55,440 --> 00:16:58,840 Speaker 1: soup in the evening, but that's it, and she works 282 00:16:58,880 --> 00:17:01,680 Speaker 1: even food don't interest her. I I would not, I 283 00:17:01,720 --> 00:17:04,680 Speaker 1: would be so cranky. I know, I don't know how 284 00:17:04,840 --> 00:17:08,400 Speaker 1: my I need food to my brain to work better. 285 00:17:08,560 --> 00:17:10,919 Speaker 1: I know, but I mean, I'm saying this about a 286 00:17:10,920 --> 00:17:13,920 Speaker 1: brain expert. So maybe she knows something. She's a hundred 287 00:17:13,920 --> 00:17:16,800 Speaker 1: and two. She figured something out. I guess I'm just 288 00:17:16,800 --> 00:17:20,359 Speaker 1: gonna have to sacrifice longevity for a cheeseburger. Seems that 289 00:17:20,480 --> 00:17:22,720 Speaker 1: she won a Nobel prize. She's probably figured out a 290 00:17:22,760 --> 00:17:26,840 Speaker 1: lot of things. The couple and she was also never 291 00:17:26,880 --> 00:17:30,720 Speaker 1: married ye that time, exactly the same father who did 292 00:17:30,720 --> 00:17:34,399 Speaker 1: not want her to pursue science education. She saw us 293 00:17:34,400 --> 00:17:36,720 Speaker 1: dominating the family, and she said she didn't want to 294 00:17:36,720 --> 00:17:39,160 Speaker 1: play second fiddle to a man, so she never married. Yeah. 295 00:17:39,200 --> 00:17:41,679 Speaker 1: She said that really the only difference between men and 296 00:17:41,720 --> 00:17:44,480 Speaker 1: women is that are while our brains are exactly the same, 297 00:17:44,480 --> 00:17:49,400 Speaker 1: men have just been able to physically overpower women. Mm hmm, 298 00:17:50,160 --> 00:17:54,080 Speaker 1: another podcast for another time. Indeed, so we've talked about 299 00:17:54,119 --> 00:17:56,119 Speaker 1: these four and obviously there are so many more, and 300 00:17:56,160 --> 00:18:00,200 Speaker 1: I would invite listeners to send in um any need 301 00:18:00,520 --> 00:18:04,040 Speaker 1: female scientists they'd like. It's to highlight in later episodes 302 00:18:04,040 --> 00:18:08,600 Speaker 1: in the listener males segments. But before we sign off, 303 00:18:08,640 --> 00:18:11,680 Speaker 1: the question is how do we how do we support 304 00:18:11,720 --> 00:18:14,119 Speaker 1: women in science better? Right? You talked about role models 305 00:18:14,160 --> 00:18:16,320 Speaker 1: earlier and uh in a column that appeared in The 306 00:18:16,359 --> 00:18:19,760 Speaker 1: Guardian in July, doctors Natalie Pedarelli and I hope I 307 00:18:19,760 --> 00:18:24,720 Speaker 1: don't butcher this woman's name, Syrian Syrian Sumner say that 308 00:18:24,760 --> 00:18:28,480 Speaker 1: the problem isn't necessarily attracting girls and women to scientific fields, 309 00:18:28,520 --> 00:18:31,000 Speaker 1: it's just keeping them there, which you know, we touched 310 00:18:31,000 --> 00:18:35,560 Speaker 1: on earlier. They attribute this to sort of a disconnect 311 00:18:35,600 --> 00:18:39,880 Speaker 1: between um men and women family life. Women are sort 312 00:18:39,920 --> 00:18:43,760 Speaker 1: of more expected, they argue to make compromises as far 313 00:18:43,800 --> 00:18:46,919 Speaker 1: as career and family's concern. Yes, at off ramp, and 314 00:18:47,000 --> 00:18:50,520 Speaker 1: especially if they're in academia. I can understand that it 315 00:18:50,640 --> 00:18:54,119 Speaker 1: probably would be a challenge to try to, you know, 316 00:18:54,440 --> 00:18:59,000 Speaker 1: chase down tenure, publishing papers, constantly doing research in addition 317 00:18:59,119 --> 00:19:04,359 Speaker 1: to teaching. Obviously, academia is one small part of what 318 00:19:04,400 --> 00:19:07,480 Speaker 1: you can do in STEM fields, but that might be 319 00:19:07,520 --> 00:19:11,400 Speaker 1: another part. Even just educating women about all the opportunities 320 00:19:11,440 --> 00:19:15,800 Speaker 1: available to them that listen up pay that more than 321 00:19:15,880 --> 00:19:20,600 Speaker 1: non stem fields. I don't think I would be successful. Hey, 322 00:19:20,800 --> 00:19:26,600 Speaker 1: you know what, Caroline stopped buying into the stereotype threat. Okay, 323 00:19:25,960 --> 00:19:30,159 Speaker 1: it's seven years of not being good at science, but 324 00:19:30,240 --> 00:19:33,359 Speaker 1: you know that's okay. I guess we can learn. And 325 00:19:33,960 --> 00:19:36,879 Speaker 1: there are organizations out there, like the Association for Women 326 00:19:36,880 --> 00:19:41,040 Speaker 1: in Science, which was founded in ninete to fight for 327 00:19:41,160 --> 00:19:44,919 Speaker 1: equity in in stem and close that gender gap. But 328 00:19:45,320 --> 00:19:48,679 Speaker 1: over and over again, the research has shown that the 329 00:19:48,840 --> 00:19:54,480 Speaker 1: change probably needs to happen in the classrooms early on. Right, UM, 330 00:19:54,560 --> 00:19:59,560 Speaker 1: we've talked about gender roles, gender stereotypes, and priming before UM. 331 00:19:59,640 --> 00:20:02,560 Speaker 1: The OR, an association of university women, released a report 332 00:20:02,560 --> 00:20:06,639 Speaker 1: in that said women are shaped by their learning environments. 333 00:20:06,640 --> 00:20:10,199 Speaker 1: They found that when teachers before a test said that 334 00:20:10,320 --> 00:20:14,320 Speaker 1: boys and girls perform equally on math tests, the girls 335 00:20:14,320 --> 00:20:18,760 Speaker 1: did better. They they did perform more equally. So encourage 336 00:20:18,760 --> 00:20:22,360 Speaker 1: girls to do better. Yeah, breakdown, break down the stereotypes, 337 00:20:22,440 --> 00:20:25,919 Speaker 1: because if half of those stem degrees are going to women. 338 00:20:26,600 --> 00:20:31,640 Speaker 1: Obviously we can do just fine science of that anyway. 339 00:20:31,760 --> 00:20:33,560 Speaker 1: Female scientists out there, I know there are a lot 340 00:20:33,600 --> 00:20:35,119 Speaker 1: of you listening because a lot of you have emailed 341 00:20:35,119 --> 00:20:38,480 Speaker 1: in requesting this topic. Let us know what what do 342 00:20:38,560 --> 00:20:41,639 Speaker 1: you think about being a woman in science? Other female 343 00:20:41,680 --> 00:20:44,639 Speaker 1: scientists that we should toss out there? And did anyone 344 00:20:44,680 --> 00:20:47,600 Speaker 1: try to discourage you from pursuing scientific career? I mean, 345 00:20:47,600 --> 00:20:48,960 Speaker 1: I know a lot of these women we talked about 346 00:20:48,960 --> 00:20:51,760 Speaker 1: whose families discouraged them were from way back when. But 347 00:20:51,840 --> 00:20:53,480 Speaker 1: I mean, did your family look at you cross side 348 00:20:53,480 --> 00:20:55,719 Speaker 1: when you said you didn't want to go after an 349 00:20:55,760 --> 00:20:58,399 Speaker 1: English degree you wanted to be a scientist? Scientist? Let 350 00:20:58,480 --> 00:21:01,280 Speaker 1: us know. Our email is mom Stuff at how stuff 351 00:21:01,280 --> 00:21:03,360 Speaker 1: works dot com and we got a couple of emails 352 00:21:03,400 --> 00:21:09,560 Speaker 1: here in response to our two podcasts exploring gender and height, 353 00:21:10,119 --> 00:21:13,480 Speaker 1: One from a men, one from a woman. Who would 354 00:21:13,480 --> 00:21:16,040 Speaker 1: you like to hear from first? Caroline? Ladies first? That's 355 00:21:16,119 --> 00:21:23,399 Speaker 1: ladies first. Sure this comes from Diana and she says, 356 00:21:24,280 --> 00:21:26,880 Speaker 1: I'm five eleven, so I get remarks about my height 357 00:21:26,920 --> 00:21:29,120 Speaker 1: a lot. I'm a teller at a bank, but we're 358 00:21:29,119 --> 00:21:31,400 Speaker 1: provided with high stools to sit on while we work. 359 00:21:31,520 --> 00:21:34,560 Speaker 1: They boost up the shorter tellers, but they make me shorter. 360 00:21:34,680 --> 00:21:36,560 Speaker 1: So I always stand when people are at my window. 361 00:21:36,960 --> 00:21:39,400 Speaker 1: People say you must be on the step stool back there. 362 00:21:39,800 --> 00:21:42,560 Speaker 1: I just smile and say, no, I'm really this tall. 363 00:21:42,640 --> 00:21:45,080 Speaker 1: My husband is six three, so I'm looking forward to 364 00:21:45,080 --> 00:21:48,000 Speaker 1: having tall children. And my dating experience, I've found that 365 00:21:48,040 --> 00:21:51,000 Speaker 1: some men are intimidated by tall women. I am white, 366 00:21:51,000 --> 00:21:53,800 Speaker 1: but white men have never approached me. I had a 367 00:21:53,840 --> 00:21:56,800 Speaker 1: white male coworker tell me once you're pretty, you're just 368 00:21:56,960 --> 00:22:00,800 Speaker 1: too tall. However, he doesn't seem to bother men of 369 00:22:00,840 --> 00:22:05,000 Speaker 1: other races. It's an interesting observation. It is interesting. Um 370 00:22:05,040 --> 00:22:08,200 Speaker 1: Adam writes to us about height as well. He says 371 00:22:08,240 --> 00:22:10,639 Speaker 1: that he has over six feet and I was wondering 372 00:22:10,640 --> 00:22:13,320 Speaker 1: why you didn't address the giant stereotype with tall men. 373 00:22:13,760 --> 00:22:16,320 Speaker 1: Although short men are viewed as more aggressive, tall men 374 00:22:16,400 --> 00:22:20,080 Speaker 1: have negative views associated with them, as evidenced in David 375 00:22:20,119 --> 00:22:23,280 Speaker 1: and Goliath, Jack and the Beanstalk, and of Mice and Men. 376 00:22:24,000 --> 00:22:27,120 Speaker 1: Men of stature have been throughout history and literature viewed 377 00:22:27,160 --> 00:22:30,520 Speaker 1: as intimidating, awkward, and a tad, dumb and sometimes arrogant, 378 00:22:30,640 --> 00:22:33,119 Speaker 1: like a bully. I know this is better than being 379 00:22:33,200 --> 00:22:36,600 Speaker 1: viewed like Joe Pesci characters for being short. I still 380 00:22:36,640 --> 00:22:38,880 Speaker 1: find I have to contend with stereotypes and on occasion 381 00:22:39,000 --> 00:22:41,320 Speaker 1: risk being turned down for something in favor of someone 382 00:22:41,440 --> 00:22:46,480 Speaker 1: viewed as more of an underdog joint stereotype. Yeah, I 383 00:22:46,480 --> 00:22:48,760 Speaker 1: mean there's on either side of the spectrum. If you're 384 00:22:48,760 --> 00:22:51,680 Speaker 1: extremely taller extremely short, I can imagine that there are 385 00:22:52,280 --> 00:22:56,240 Speaker 1: stereotypes a plenty. Sure, So, if you have any thoughts 386 00:22:56,240 --> 00:22:58,760 Speaker 1: to send our way again, our email addresses mom Stuff 387 00:22:58,800 --> 00:23:01,880 Speaker 1: at how stuff works Com. You can also head over 388 00:23:02,000 --> 00:23:05,800 Speaker 1: to Facebook find us there, and also follow us on 389 00:23:05,840 --> 00:23:10,640 Speaker 1: Twitter at mom Stuff Podcast. And lastly, during the week 390 00:23:10,840 --> 00:23:13,840 Speaker 1: you can read the blog It's stuff Mom Never told 391 00:23:13,880 --> 00:23:19,800 Speaker 1: You from how Stuff works dot com. Be sure to 392 00:23:19,920 --> 00:23:22,680 Speaker 1: check out our new video podcast, Stuff from the Future. 393 00:23:23,040 --> 00:23:25,320 Speaker 1: Join how Stuff Work staff as we explore the most 394 00:23:25,320 --> 00:23:29,680 Speaker 1: promising and perplexing possibilities of tomorrow. The How Stuff Works 395 00:23:29,680 --> 00:23:35,240 Speaker 1: I Fine app has a ride. Download it today on iTunes, 396 00:23:37,720 --> 00:23:40,320 Speaker 1: brought to you by the reinvented two thousand twelve camera. 397 00:23:40,640 --> 00:23:41,840 Speaker 1: It's ready, are you