1 00:00:03,200 --> 00:00:06,520 Speaker 1: Welcome to Stuff Mom Never told You from housetupp Works 2 00:00:06,519 --> 00:00:14,880 Speaker 1: dot com. Hello, and welcome to the podcast. I'm Kristen, 3 00:00:15,040 --> 00:00:19,000 Speaker 1: and I'm Caroline and I'm Laura. That's right, everybody, for 4 00:00:19,160 --> 00:00:22,520 Speaker 1: this second installment of our Women in Stem series that 5 00:00:22,560 --> 00:00:26,040 Speaker 1: we're doing. This is the technology episode in Caroline and 6 00:00:26,079 --> 00:00:29,040 Speaker 1: I are thrilled to have on. Lauren vogel Baum, co 7 00:00:29,240 --> 00:00:33,519 Speaker 1: host of the House up Works podcasts Plural Tech Stuff 8 00:00:33,880 --> 00:00:37,120 Speaker 1: and for thinking that is entirely correct. Lauren, thanks so 9 00:00:37,200 --> 00:00:39,479 Speaker 1: much for coming on. You're so welcome. Thank you for 10 00:00:39,520 --> 00:00:42,360 Speaker 1: having me here. Well, we've got a lot of stuff 11 00:00:42,360 --> 00:00:46,720 Speaker 1: to talk about with women in tech because the situation 12 00:00:46,800 --> 00:00:49,040 Speaker 1: is a little bleak. Yeah, we've got some sad stories 13 00:00:49,040 --> 00:00:51,000 Speaker 1: for you. We do have some sad stories, but some 14 00:00:51,080 --> 00:00:56,240 Speaker 1: happy stories. Yeah, Because if we look at women in 15 00:00:56,320 --> 00:01:00,600 Speaker 1: tech going back to the sixties, it started all right, 16 00:01:01,920 --> 00:01:04,080 Speaker 1: even even back to the forties. Yeah, I mean some 17 00:01:04,160 --> 00:01:06,360 Speaker 1: of those some of the first computer programmers who were 18 00:01:06,400 --> 00:01:10,520 Speaker 1: working on the big old mechanical punch tape machines were women. 19 00:01:10,560 --> 00:01:12,680 Speaker 1: They're a group of six of them are trying to 20 00:01:12,720 --> 00:01:15,240 Speaker 1: get together and make a documentary about their experience because 21 00:01:15,240 --> 00:01:18,200 Speaker 1: their story really hasn't been told. And you know, programming 22 00:01:18,200 --> 00:01:21,600 Speaker 1: at the time was was considered akin to clerical work. Um, 23 00:01:21,640 --> 00:01:24,160 Speaker 1: it was considered a really a really promising field for 24 00:01:24,200 --> 00:01:27,360 Speaker 1: women to be in. There was this really great quote 25 00:01:27,440 --> 00:01:29,800 Speaker 1: from from one Grace Hopper, who will talk more about 26 00:01:29,840 --> 00:01:33,679 Speaker 1: in a moment in Cosmo around saying that programming is 27 00:01:33,800 --> 00:01:35,800 Speaker 1: just like planning a dinner. You have to plan ahead 28 00:01:35,800 --> 00:01:38,000 Speaker 1: and schedule everything so that's ready when you need it. 29 00:01:38,280 --> 00:01:41,759 Speaker 1: Women are naturals at computer programming. Did you know that, Caroline, Well, 30 00:01:41,760 --> 00:01:45,679 Speaker 1: there you go. No, I took like a coding class, 31 00:01:46,080 --> 00:01:48,760 Speaker 1: and I would not have thought to compare it to 32 00:01:48,800 --> 00:01:51,520 Speaker 1: dinner to making a dinner. But then again, when is 33 00:01:51,520 --> 00:01:53,160 Speaker 1: the last time that I ever planned a dinner party? 34 00:01:53,320 --> 00:01:55,320 Speaker 1: So I was about to say, I'm completely terrified of 35 00:01:55,400 --> 00:01:58,440 Speaker 1: making dinner and coding both, So I'm not really sure. 36 00:01:58,480 --> 00:02:01,160 Speaker 1: I mean, maybe maybe it's accurate. I would, Well, ladies, 37 00:02:01,880 --> 00:02:04,560 Speaker 1: I'm pretty good at making a cast role, so maybe 38 00:02:04,600 --> 00:02:07,800 Speaker 1: I should transition into coding. We will come over and 39 00:02:07,880 --> 00:02:12,240 Speaker 1: you can cook for us and we will code together. Perfect. Yeah, 40 00:02:12,280 --> 00:02:14,320 Speaker 1: it'll be like a like a knitting group, except with 41 00:02:14,680 --> 00:02:17,520 Speaker 1: way more computers. Well did we mention though, that that 42 00:02:17,600 --> 00:02:21,359 Speaker 1: Grace Hopper quote about planning dinner actually came out of 43 00:02:21,560 --> 00:02:27,040 Speaker 1: none other than Cosmopolitan magazine in nineteen sixties seven because 44 00:02:27,720 --> 00:02:31,240 Speaker 1: moving out of the forties, obviously, um, when you know, 45 00:02:31,240 --> 00:02:33,560 Speaker 1: we have these six women who are working on the eniac. 46 00:02:33,840 --> 00:02:37,400 Speaker 1: But by the nineteen sixties, computers are really starting to 47 00:02:37,440 --> 00:02:41,680 Speaker 1: become more of uh their own industry. They're actually fields 48 00:02:41,720 --> 00:02:44,880 Speaker 1: developing around that. And there was this article in Cosmo 49 00:02:45,120 --> 00:02:49,320 Speaker 1: about the quote unquote computer girls, because at the time 50 00:02:49,400 --> 00:02:52,720 Speaker 1: it was thought that, yeah, this low level quote unquote 51 00:02:52,720 --> 00:02:56,400 Speaker 1: clerical work would be great for women. That seems like 52 00:02:56,440 --> 00:03:01,560 Speaker 1: a missed Halloween costume opportunity computer girls. So in the 53 00:03:01,639 --> 00:03:06,440 Speaker 1: nineteen sixties, So could you say, Lauren that Grace Hopper 54 00:03:06,480 --> 00:03:09,360 Speaker 1: at the time was kind of like a Cheryl Sandberg 55 00:03:09,400 --> 00:03:12,360 Speaker 1: or Marissa Meyer of her day. She was one of 56 00:03:12,400 --> 00:03:16,640 Speaker 1: the leading women, a role model for other women in computing. 57 00:03:16,800 --> 00:03:19,760 Speaker 1: She absolutely was. And I should mention that Grace Hopper, 58 00:03:19,919 --> 00:03:23,440 Speaker 1: that's Rear Admiral doctor Grace Murray Hopper to you, um, 59 00:03:23,639 --> 00:03:27,120 Speaker 1: because not not only a doctorate in mathematics, but also 60 00:03:27,400 --> 00:03:29,480 Speaker 1: in the Navy, rose to the rank of rear admiral 61 00:03:29,560 --> 00:03:31,840 Speaker 1: by the end of her career. You know, she was 62 00:03:31,880 --> 00:03:35,000 Speaker 1: the third person to work on programming um the the 63 00:03:35,080 --> 00:03:37,720 Speaker 1: Mark one, the Harvard Mark one computer in the nineteen 64 00:03:37,800 --> 00:03:41,400 Speaker 1: forties at Harvard's Craft Laboratory, which was I mean it 65 00:03:41,440 --> 00:03:45,520 Speaker 1: was essentially a pocket calculator, but a fifty one foot 66 00:03:45,560 --> 00:03:47,440 Speaker 1: pocket calculator, and it was really one of the first 67 00:03:47,480 --> 00:03:50,400 Speaker 1: machines that could do that kind of calculation automatically. So 68 00:03:50,440 --> 00:03:52,960 Speaker 1: it was a big deal. And a few years later, 69 00:03:53,040 --> 00:03:57,200 Speaker 1: when electronic computers began replacing the mechanical punch tape sorts 70 00:03:57,200 --> 00:03:59,840 Speaker 1: of things, she was on the team that developed the 71 00:04:00,240 --> 00:04:03,480 Speaker 1: binary code compiler, and and compilers are a little bit 72 00:04:03,480 --> 00:04:07,160 Speaker 1: like a translation program for computers. They help a computer 73 00:04:07,560 --> 00:04:10,440 Speaker 1: and a person interacting with it. You know, it's a 74 00:04:10,520 --> 00:04:12,960 Speaker 1: lot easier for for the programmer to write in a 75 00:04:13,040 --> 00:04:16,000 Speaker 1: language that isn't directly accessible to the computer, and so 76 00:04:16,040 --> 00:04:21,240 Speaker 1: that compilers is helping translate. It's the basis for modern computing. Yeah, 77 00:04:21,240 --> 00:04:24,200 Speaker 1: when I was reading about this, it sounds like such 78 00:04:24,520 --> 00:04:28,440 Speaker 1: a basic principle of computing today. But at the time, 79 00:04:29,000 --> 00:04:32,800 Speaker 1: Hopper has said that people's minds were so blown by 80 00:04:32,880 --> 00:04:35,279 Speaker 1: the very notion that a computer could do this that 81 00:04:35,320 --> 00:04:38,080 Speaker 1: when she would talk about making this compiler. People wouldn't 82 00:04:38,120 --> 00:04:41,160 Speaker 1: believe her. Yeah. Yeah, they were like, they were like, no, never, never, 83 00:04:41,200 --> 00:04:43,240 Speaker 1: that will never happen. How could you possibly talk about 84 00:04:43,279 --> 00:04:45,080 Speaker 1: that kind of thing? You know, it reminds me of 85 00:04:45,080 --> 00:04:48,120 Speaker 1: of stories about it all Lovelace And when Charles Babbage, 86 00:04:48,240 --> 00:04:51,159 Speaker 1: this was way way back in the eight hundreds, I believe, 87 00:04:51,640 --> 00:04:53,200 Speaker 1: he was talking to her about this idea that he 88 00:04:53,240 --> 00:04:56,359 Speaker 1: had for an analytical machine, and she said, you know, 89 00:04:56,600 --> 00:05:00,680 Speaker 1: that could do more than just than just computations or calculations. 90 00:05:00,680 --> 00:05:02,919 Speaker 1: You could use math to talk to the computer and 91 00:05:03,000 --> 00:05:05,279 Speaker 1: have it talked to you back in text or in 92 00:05:05,360 --> 00:05:09,480 Speaker 1: pictures or in music. And everyone was like how just 93 00:05:09,600 --> 00:05:11,320 Speaker 1: you know, like if I could insert a graphic of 94 00:05:11,360 --> 00:05:13,400 Speaker 1: a giant explosion, that would be what I would put 95 00:05:13,560 --> 00:05:16,480 Speaker 1: right here. She was blowing people's minds because I mean, 96 00:05:16,839 --> 00:05:21,320 Speaker 1: you know, women back then, they didn't think thoughts like that. 97 00:05:21,560 --> 00:05:24,400 Speaker 1: Come on, and then certainly not mathematical thoughts, because everyone 98 00:05:24,440 --> 00:05:27,120 Speaker 1: knows that girls are bad at math, right exactly, But 99 00:05:28,120 --> 00:05:31,560 Speaker 1: they are good at low level clerical work, which is 100 00:05:31,600 --> 00:05:35,360 Speaker 1: why they were the first programmers. Yeah, until it became 101 00:05:35,520 --> 00:05:40,640 Speaker 1: more masculinized. Yeah, supposedly this and and this this quote 102 00:05:40,760 --> 00:05:43,560 Speaker 1: that we've been talking about from that Cosmo article is 103 00:05:43,680 --> 00:05:46,120 Speaker 1: um from a really great piece published by Brenda D. 104 00:05:46,320 --> 00:05:50,520 Speaker 1: Frank through Stanford University's Claim and Institute for Gender Research. UM. 105 00:05:50,560 --> 00:05:54,080 Speaker 1: And it's in the article she's describing how male programmers 106 00:05:54,080 --> 00:05:56,680 Speaker 1: at the time we're seeking to increase the prestige of 107 00:05:56,680 --> 00:06:02,359 Speaker 1: the industry a k a. To defeminize it um, which cheery. Um, 108 00:06:02,400 --> 00:06:04,160 Speaker 1: you know, I mean this was also and some of 109 00:06:04,240 --> 00:06:06,160 Speaker 1: the examples that I've read from the time are just 110 00:06:06,200 --> 00:06:09,000 Speaker 1: so mad menesque that I can't even believe that it's 111 00:06:09,000 --> 00:06:11,720 Speaker 1: a thing that really existed. Um. Not that mad Men is, 112 00:06:12,080 --> 00:06:14,919 Speaker 1: I mean, you know, not a completely inaccurate, but still 113 00:06:15,320 --> 00:06:17,440 Speaker 1: it's just so weird to me. Yeah, that that men 114 00:06:17,480 --> 00:06:20,240 Speaker 1: in the in the programming field started shutting women out 115 00:06:20,279 --> 00:06:24,480 Speaker 1: by by putting in educational requirements, um, using mail networks 116 00:06:24,520 --> 00:06:27,360 Speaker 1: to advertise jobs like frats and lodges stuff like that 117 00:06:27,360 --> 00:06:31,159 Speaker 1: that women wouldn't be a part of and uh, and 118 00:06:31,279 --> 00:06:35,640 Speaker 1: using personality tests that described programmers as disinterested in people 119 00:06:35,839 --> 00:06:40,560 Speaker 1: and disliking activities involving close personal interaction. And hence we 120 00:06:40,680 --> 00:06:45,120 Speaker 1: have the stereotype of the antisocial male computer programmer who 121 00:06:45,160 --> 00:06:47,479 Speaker 1: doesn't get out of his basement often, right, But I 122 00:06:47,480 --> 00:06:52,159 Speaker 1: mean it sounds like an incredibly literal, clear linear direct 123 00:06:52,960 --> 00:06:56,600 Speaker 1: uh predecessor to the culture that we're going to talk 124 00:06:56,640 --> 00:07:00,560 Speaker 1: about in a little bit as far as programmer things 125 00:07:00,600 --> 00:07:04,200 Speaker 1: like that. Just the overall kind of taking away the 126 00:07:04,240 --> 00:07:08,200 Speaker 1: welcome mat for women. Yeah, And it's unfortunate because today 127 00:07:08,839 --> 00:07:12,480 Speaker 1: so much of a conversation and the emphasis in tech 128 00:07:12,640 --> 00:07:14,960 Speaker 1: that you hear over and over and over again and 129 00:07:15,000 --> 00:07:19,520 Speaker 1: not just in women only circles, is how to get 130 00:07:19,520 --> 00:07:24,080 Speaker 1: that welcome back back? Right? Yeah? Um, so what do 131 00:07:24,120 --> 00:07:26,320 Speaker 1: what do the statistics look like though? In terms of 132 00:07:27,360 --> 00:07:31,760 Speaker 1: women who are pursuing computer science, Well, it starts out 133 00:07:31,960 --> 00:07:34,440 Speaker 1: not so bad in high school, I would think, I mean, 134 00:07:34,480 --> 00:07:38,360 Speaker 1: people talk about this pipeline of women in tech and computing, 135 00:07:38,680 --> 00:07:41,679 Speaker 1: and that the pipeline leads you from you know, middle school, 136 00:07:41,760 --> 00:07:43,400 Speaker 1: high school all the way through grad school and then 137 00:07:43,400 --> 00:07:46,480 Speaker 1: it has leaks, and so it starts out not so bad. 138 00:07:46,680 --> 00:07:53,200 Speaker 1: Umi of AP calculus test takers were female in It's 139 00:07:53,240 --> 00:07:58,400 Speaker 1: not quite as great for the computer science classes though. Yeah. 140 00:07:58,480 --> 00:08:03,200 Speaker 1: Compared to calculus, only nineteen percent of AP computer science 141 00:08:03,240 --> 00:08:07,240 Speaker 1: test takers were women in two thousand eleven. And yeah, 142 00:08:07,280 --> 00:08:13,360 Speaker 1: I'm gonna be honest, an AP computer science test sounds 143 00:08:13,480 --> 00:08:16,400 Speaker 1: terrifying to me. Well, but also, I mean, I think 144 00:08:16,400 --> 00:08:17,960 Speaker 1: that has a lot to do with the fact that, 145 00:08:18,000 --> 00:08:21,440 Speaker 1: like I, you know, personally, I didn't grow up even 146 00:08:21,480 --> 00:08:24,760 Speaker 1: thinking math or science was an option. Yeah, I mean, 147 00:08:24,800 --> 00:08:26,760 Speaker 1: it didn't. It didn't appeal to me. It didn't make 148 00:08:26,800 --> 00:08:29,640 Speaker 1: sense to me. I was always word driven. But that's 149 00:08:29,640 --> 00:08:33,520 Speaker 1: also kind of the direction that I was sort of push. Yeah. Yeah, 150 00:08:33,559 --> 00:08:35,520 Speaker 1: I feel like even from elementary school, and I'm sure 151 00:08:35,559 --> 00:08:38,559 Speaker 1: that you guys have talked about statistics surrounding this, also 152 00:08:38,760 --> 00:08:42,400 Speaker 1: that that that girls are are encouraged towards language driven 153 00:08:42,400 --> 00:08:45,000 Speaker 1: things in a way from science driven driven things most 154 00:08:45,000 --> 00:08:47,160 Speaker 1: of the time. I mean, I certainly, you know, like 155 00:08:47,200 --> 00:08:49,560 Speaker 1: all of my test scores were about even on math 156 00:08:49,679 --> 00:08:52,720 Speaker 1: and language. But until I started getting like further on 157 00:08:52,760 --> 00:08:55,319 Speaker 1: in high school and language went but chaw. So, I mean, 158 00:08:55,360 --> 00:08:57,680 Speaker 1: you know, I should say that I'm not I'm not 159 00:08:57,720 --> 00:09:00,160 Speaker 1: a programmer, you know, I'm a I'm a tech journalist. 160 00:09:00,280 --> 00:09:05,240 Speaker 1: But that's certainly not being directly involved in the tech industry. Well, 161 00:09:05,280 --> 00:09:08,679 Speaker 1: speaking of being directly involved in the tech industry, it's 162 00:09:08,679 --> 00:09:13,400 Speaker 1: also fascinating to see the erosion of the number of 163 00:09:13,880 --> 00:09:17,840 Speaker 1: women in college who are not just expressing interest at 164 00:09:17,880 --> 00:09:20,559 Speaker 1: the outset of wanting to go onto computer science, but 165 00:09:20,640 --> 00:09:23,240 Speaker 1: of the women who actually stick with it and complete 166 00:09:23,280 --> 00:09:27,079 Speaker 1: that computer science degree. Because in the mid eighties there 167 00:09:27,080 --> 00:09:29,720 Speaker 1: were a fair number of us who were doing that. 168 00:09:29,800 --> 00:09:34,040 Speaker 1: Thirty seven percent of computer science undergrads were women, and 169 00:09:34,080 --> 00:09:38,880 Speaker 1: we actually peaked in computer science academia in the nineteen eighties. 170 00:09:39,240 --> 00:09:43,800 Speaker 1: Coincidence that that's around the time that The Wizard came 171 00:09:43,800 --> 00:09:48,520 Speaker 1: out and War Games starring Ali Sheey that's right, Fred Savage? 172 00:09:48,920 --> 00:09:51,960 Speaker 1: Uh and what's her name for Rilokaili was in was 173 00:09:52,000 --> 00:09:55,520 Speaker 1: in that movie with Fred Savage? Anyway, I did um. 174 00:09:55,520 --> 00:09:57,959 Speaker 1: But Yeah, it's crazy that, like the numbers were, Okay, 175 00:09:57,960 --> 00:10:01,400 Speaker 1: thirty seven percent is not percent, but it's still not 176 00:10:01,520 --> 00:10:06,959 Speaker 1: too shabby considering Yeah, as of it was only eight percent. 177 00:10:07,200 --> 00:10:10,520 Speaker 1: And if we are just looking at interest in majoring 178 00:10:10,520 --> 00:10:14,240 Speaker 1: in comps I, there is a seventy nine percent decline 179 00:10:14,800 --> 00:10:18,199 Speaker 1: from two thousand to two thousand and eleven. It's crazy 180 00:10:18,240 --> 00:10:24,320 Speaker 1: what happened. Yeah, I is serious. Well, and how that 181 00:10:24,360 --> 00:10:28,160 Speaker 1: translates into actually jobby job land if we look at 182 00:10:28,200 --> 00:10:31,480 Speaker 1: the career landscape, obviously it's going to be dominated by 183 00:10:31,520 --> 00:10:35,440 Speaker 1: dudes because of that. Yeah, only about a quarter of 184 00:10:35,440 --> 00:10:38,680 Speaker 1: professional computing occupations were held by women in the US, 185 00:10:38,760 --> 00:10:44,520 Speaker 1: and that is down from thirty six percent in And 186 00:10:44,679 --> 00:10:48,959 Speaker 1: if you look at high powered positions, only of chief 187 00:10:49,000 --> 00:10:52,760 Speaker 1: information officer positions at Fortune to fifty companies were held 188 00:10:52,760 --> 00:10:55,800 Speaker 1: by women in and just to get a sense of 189 00:10:55,920 --> 00:11:01,120 Speaker 1: the job breakdown within the industry, two and five developers 190 00:11:01,160 --> 00:11:05,760 Speaker 1: are women, but seventy percent of software developers are men. 191 00:11:05,920 --> 00:11:08,000 Speaker 1: And then there's this other issue that we'll talk about 192 00:11:08,040 --> 00:11:12,000 Speaker 1: a little bit more in terms of startups because as 193 00:11:12,000 --> 00:11:15,120 Speaker 1: of two thousand and ten, only six percent of venture 194 00:11:15,160 --> 00:11:20,199 Speaker 1: capital back startups were headed by women. And then again 195 00:11:20,280 --> 00:11:23,800 Speaker 1: not to make this the most suppressing podcast ever, but 196 00:11:23,920 --> 00:11:27,319 Speaker 1: even okay, for women who have gone to college, they 197 00:11:27,400 --> 00:11:30,720 Speaker 1: made it through their classes, maybe being the only female, 198 00:11:30,720 --> 00:11:33,640 Speaker 1: they got their computer science degree, they got their computer job, 199 00:11:34,080 --> 00:11:37,960 Speaker 1: and then we have the retention problem. Yeah, it seems 200 00:11:38,000 --> 00:11:40,400 Speaker 1: like what I what I was reading about these retention issues, 201 00:11:40,679 --> 00:11:46,000 Speaker 1: it seems like women are kind of suffering silently, maybe 202 00:11:46,040 --> 00:11:49,600 Speaker 1: in these kind of unwelcoming male dominated cultures, and then 203 00:11:50,200 --> 00:11:54,120 Speaker 1: leaving kind of quickly or maybe even not so silently 204 00:11:54,160 --> 00:11:56,160 Speaker 1: these days with blogs, I've read a lot of really 205 00:11:56,200 --> 00:11:58,640 Speaker 1: angry blog posts from women who are just going like, 206 00:11:58,679 --> 00:12:02,080 Speaker 1: I'm fed up with this, never mind, never mind, I'm out. Yeah. 207 00:12:02,160 --> 00:12:05,200 Speaker 1: And as a result, the Anita Borg Institute found that 208 00:12:05,280 --> 00:12:09,560 Speaker 1: the cumulative quit rates of women in technology is more 209 00:12:09,640 --> 00:12:12,560 Speaker 1: than double that of men. And when they look at 210 00:12:12,600 --> 00:12:17,520 Speaker 1: the primary challenges that Anita Borg highlights, it's very similar 211 00:12:17,559 --> 00:12:21,280 Speaker 1: to other issues that echo throughout STEM fields, such as 212 00:12:21,400 --> 00:12:25,679 Speaker 1: lack of opportunities for recognition and advancement, challenges of work 213 00:12:25,760 --> 00:12:31,000 Speaker 1: life demands, um isolation, and unconscious biases that women may 214 00:12:31,000 --> 00:12:37,040 Speaker 1: be experiencing in more male dominated workplaces. So we're gonna 215 00:12:37,080 --> 00:12:40,600 Speaker 1: dig though more into those issues, tease them apart, see 216 00:12:40,640 --> 00:12:44,600 Speaker 1: whether or not these direct factors really are leading to 217 00:12:45,000 --> 00:12:48,240 Speaker 1: this pipeline problem for women in tech. When we come 218 00:12:48,400 --> 00:12:52,480 Speaker 1: right back from a quick break, Okay, so we told 219 00:12:52,520 --> 00:12:53,800 Speaker 1: you that we were going to get into some of 220 00:12:53,840 --> 00:12:57,640 Speaker 1: the culture and the reasoning behind this, the steep decline 221 00:12:57,679 --> 00:13:02,400 Speaker 1: in women's interest and participation in the tech fields. People 222 00:13:02,480 --> 00:13:06,760 Speaker 1: keep tossing this term around and it is programmer programming. 223 00:13:07,040 --> 00:13:11,120 Speaker 1: What does a programmer have to do with women? So 224 00:13:11,320 --> 00:13:15,520 Speaker 1: there's this perception that that programmers these days are kind 225 00:13:15,520 --> 00:13:18,360 Speaker 1: of stuck in the college mentality of like, yeah, we're 226 00:13:18,360 --> 00:13:21,040 Speaker 1: going to get some beer and some ladies and hang 227 00:13:21,080 --> 00:13:24,080 Speaker 1: out on couches and do all our programming there. And 228 00:13:24,200 --> 00:13:26,160 Speaker 1: I mean, you know, not helped by the fact that like, 229 00:13:26,559 --> 00:13:28,560 Speaker 1: what was that line the Cloud put out at some 230 00:13:28,640 --> 00:13:32,959 Speaker 1: point for for hiring, It was terrific, want to brow 231 00:13:33,000 --> 00:13:37,880 Speaker 1: down and crush some code, bro. I can't even that's 232 00:13:37,920 --> 00:13:40,199 Speaker 1: not even a thing that I can respond to. So 233 00:13:40,400 --> 00:13:44,040 Speaker 1: Kamashki Savarma Christian, who was the first female engineer at 234 00:13:44,080 --> 00:13:49,080 Speaker 1: the mobile ads startup ad Mob, told Business Week that quote, 235 00:13:49,080 --> 00:13:52,559 Speaker 1: the frat boy mentality among engineering men is a little 236 00:13:52,559 --> 00:13:55,640 Speaker 1: more pronounced in the startup world than in the more 237 00:13:55,880 --> 00:14:00,679 Speaker 1: mature organizations. So is it just is it a startup thing? 238 00:14:00,800 --> 00:14:05,720 Speaker 1: Is it just tech are we? Or is the programmer stereotype? Lauren? 239 00:14:05,760 --> 00:14:09,839 Speaker 1: Do you think it's just overblown? Maybe? Well, I think 240 00:14:09,880 --> 00:14:12,599 Speaker 1: it's more for for the younger startup generation than for 241 00:14:12,760 --> 00:14:14,280 Speaker 1: the older I mean, I don't think that you're going 242 00:14:14,320 --> 00:14:16,760 Speaker 1: to encounter that if you go to like Yahoo or something, 243 00:14:16,920 --> 00:14:19,680 Speaker 1: But but for certainly smaller companies where part of the 244 00:14:19,720 --> 00:14:22,120 Speaker 1: appeal is that you can be you know, that you 245 00:14:22,160 --> 00:14:24,840 Speaker 1: can dress casually and that you can be more yourself, 246 00:14:24,880 --> 00:14:26,880 Speaker 1: that you can have silly stuff in your office, and 247 00:14:26,880 --> 00:14:29,000 Speaker 1: that you don't have to be so buttoned down and 248 00:14:29,200 --> 00:14:33,880 Speaker 1: part of that in these very dude oriented offices, you know, 249 00:14:34,000 --> 00:14:37,240 Speaker 1: which there's nothing particularly wrong with if if a guy 250 00:14:37,240 --> 00:14:39,400 Speaker 1: starts a company and hires people and they happen to 251 00:14:39,440 --> 00:14:41,920 Speaker 1: be guys, but you know, just people have to be 252 00:14:41,960 --> 00:14:43,720 Speaker 1: aware of the fact that they might be making that 253 00:14:43,840 --> 00:14:47,160 Speaker 1: environment unwelcoming to people who are not exactly like them. 254 00:14:47,240 --> 00:14:48,720 Speaker 1: And and I do, I do think that it gets 255 00:14:48,720 --> 00:14:51,200 Speaker 1: overblown in the media because programmer is such a great 256 00:14:51,240 --> 00:14:53,680 Speaker 1: silly word to put up in a headline. And but 257 00:14:53,760 --> 00:14:56,680 Speaker 1: I don't I don't think it's just like dudes hiring dudes. 258 00:14:56,720 --> 00:14:58,800 Speaker 1: I mean, I also think there have been some pretty 259 00:14:58,880 --> 00:15:02,360 Speaker 1: like hor thickly stupid things that we've seen pop up 260 00:15:02,360 --> 00:15:06,920 Speaker 1: in the media that these male programming tech you know, 261 00:15:07,040 --> 00:15:11,360 Speaker 1: app developing software developing guys have said, and not just 262 00:15:11,400 --> 00:15:13,920 Speaker 1: like in the bathroom to themselves, I mean they've they've 263 00:15:13,920 --> 00:15:16,280 Speaker 1: said it like, you know, on stage. Yeah, I think 264 00:15:16,280 --> 00:15:19,280 Speaker 1: you're probably referring to an incident that happened at south 265 00:15:19,320 --> 00:15:23,680 Speaker 1: By Southwest Interactive a couple of years back, in a 266 00:15:23,720 --> 00:15:27,720 Speaker 1: presentation that was being given by this guy named Matt 267 00:15:27,800 --> 00:15:30,280 Speaker 1: Van Horne, Is this what you're referring to, Caroline, That 268 00:15:30,440 --> 00:15:32,560 Speaker 1: is what I'm referring to. And he even like before 269 00:15:32,560 --> 00:15:35,760 Speaker 1: he gives his presentation, apologizes for like, hey, guys, I'm sorry, 270 00:15:35,760 --> 00:15:39,960 Speaker 1: I'm gonna be sexist here. Here it goes. Well, it's terrific. 271 00:15:40,000 --> 00:15:42,920 Speaker 1: I love it. Whenever anyone says that that's great. Yeah, 272 00:15:43,000 --> 00:15:47,080 Speaker 1: he speaks against having a certain style of interview where 273 00:15:47,160 --> 00:15:50,080 Speaker 1: an entire committee like gangs up on a person, and 274 00:15:50,160 --> 00:15:53,120 Speaker 1: he calls that a gang bang interview. And and not 275 00:15:53,200 --> 00:15:55,840 Speaker 1: only that, he he talks about getting a job at 276 00:15:55,920 --> 00:16:01,680 Speaker 1: dig by sending bikini shots from a callender a hotty 277 00:16:01,800 --> 00:16:05,440 Speaker 1: calendar that he had made. Right, well, all right, I'll 278 00:16:05,480 --> 00:16:09,520 Speaker 1: give it to Van Horn that he did apologize profusely, 279 00:16:09,760 --> 00:16:13,000 Speaker 1: of course after this happened, because there were women getting 280 00:16:13,080 --> 00:16:17,200 Speaker 1: up from this presentation actively tweeting it, live tweeting the 281 00:16:17,320 --> 00:16:20,560 Speaker 1: entire thing and leaving, and so of course he, you know, 282 00:16:20,600 --> 00:16:23,920 Speaker 1: he puts his tail between his legs and apologizes. But 283 00:16:24,680 --> 00:16:29,880 Speaker 1: I do think that maybe this programmer culture is reflective 284 00:16:30,080 --> 00:16:34,040 Speaker 1: of a larger thing that we do see with startups 285 00:16:34,480 --> 00:16:37,680 Speaker 1: where it's not just a job, it's a lifestyle. You 286 00:16:37,720 --> 00:16:41,440 Speaker 1: go to the Google complex and they have food, they 287 00:16:41,480 --> 00:16:44,880 Speaker 1: have laundry, they have childcare. You go to Facebook here 288 00:16:44,880 --> 00:16:46,840 Speaker 1: expected to be there all night. That's something that Cheryl 289 00:16:46,840 --> 00:16:50,120 Speaker 1: Sandbury actually talks about in lean In, where she had 290 00:16:50,200 --> 00:16:54,040 Speaker 1: to kind of mentally get over the fact that she 291 00:16:54,200 --> 00:16:57,480 Speaker 1: wasn't gonna be what was it growing down and crushing 292 00:16:57,560 --> 00:17:01,600 Speaker 1: code all night with the guys, because it's just it's 293 00:17:01,640 --> 00:17:05,880 Speaker 1: physically impossible when you're you know, when you have a family, exactly. Yeah, 294 00:17:05,880 --> 00:17:08,199 Speaker 1: and so you when you do have a lot of 295 00:17:08,320 --> 00:17:11,600 Speaker 1: younger people guy or girl who might not have families, 296 00:17:11,960 --> 00:17:16,280 Speaker 1: maybe there's more leeway for a beer guzzling, you know, 297 00:17:16,680 --> 00:17:19,440 Speaker 1: this pumping. And you know, I've worked at a startup 298 00:17:19,480 --> 00:17:22,399 Speaker 1: that it was a marketing startup that had a beer fridge, 299 00:17:22,400 --> 00:17:24,160 Speaker 1: and you know, after five o'clock it was like beer 300 00:17:24,160 --> 00:17:26,359 Speaker 1: fridges open, have some beers, and you know, and no 301 00:17:26,400 --> 00:17:29,119 Speaker 1: one like got crazy, Like we weren't browing down, I 302 00:17:29,119 --> 00:17:31,560 Speaker 1: don't think, but I would love to see you try 303 00:17:31,600 --> 00:17:36,440 Speaker 1: to brow down. But Gina Trapani, who's a webin mobile 304 00:17:36,440 --> 00:17:39,480 Speaker 1: app developer, says that the whole programmer thing, Like you said, 305 00:17:39,520 --> 00:17:42,040 Speaker 1: it is a joke. Maybe it's not like an ingrained 306 00:17:42,280 --> 00:17:46,520 Speaker 1: culture that every single guy programmer lives and breathes. But 307 00:17:46,600 --> 00:17:49,000 Speaker 1: she says that it's a useful one to lend language 308 00:17:49,000 --> 00:17:52,439 Speaker 1: to a persistent issue of tech companies with overwhelmingly male staff. 309 00:17:52,480 --> 00:17:56,000 Speaker 1: She says basically that it helps women decide we're not 310 00:17:56,119 --> 00:17:58,640 Speaker 1: to work And I think, I mean, that's that's good 311 00:17:58,680 --> 00:18:01,119 Speaker 1: and that's horrible all the same time, because is that 312 00:18:01,200 --> 00:18:04,560 Speaker 1: not just contributing to a decline in women's numbers at 313 00:18:04,640 --> 00:18:07,159 Speaker 1: a lot of companies? Sure, yeah, I mean, you know, 314 00:18:07,200 --> 00:18:09,960 Speaker 1: I also think that it's just like just like any 315 00:18:10,040 --> 00:18:12,520 Speaker 1: company policy, you're going to have companies that you do 316 00:18:12,560 --> 00:18:15,399 Speaker 1: and don't want to work for. And it totally sucks. 317 00:18:15,440 --> 00:18:17,600 Speaker 1: Don't get me wrong that there's this gender divide that's 318 00:18:17,640 --> 00:18:20,120 Speaker 1: forcing part of this issue here, But you know, it's 319 00:18:20,160 --> 00:18:23,840 Speaker 1: not isolated to just sexism. Yeah, and I do think 320 00:18:23,880 --> 00:18:28,480 Speaker 1: that it's it's a good point to remember that sexist 321 00:18:28,920 --> 00:18:33,119 Speaker 1: comments and inappropriate behavior are by no means limited to 322 00:18:33,320 --> 00:18:37,000 Speaker 1: Silicon Valley. It might we might hear more about it 323 00:18:37,080 --> 00:18:39,960 Speaker 1: because the people who are hearing it are maybe more 324 00:18:39,960 --> 00:18:42,800 Speaker 1: wired and more apt to live tweeted, and in a 325 00:18:42,880 --> 00:18:46,560 Speaker 1: startup culture, maybe you feel more empowered to raise your 326 00:18:46,560 --> 00:18:48,879 Speaker 1: hand and say that's not okay, rather than if you 327 00:18:48,920 --> 00:18:51,320 Speaker 1: are a tiny cog at the bottom of a very 328 00:18:51,400 --> 00:18:56,000 Speaker 1: large org chart. So I do think there is some 329 00:18:56,040 --> 00:18:59,119 Speaker 1: caution that needs to be taken though with portraying the 330 00:18:59,160 --> 00:19:02,159 Speaker 1: tech industry is too bleak, because it's like if we 331 00:19:02,240 --> 00:19:05,280 Speaker 1: take it in the other direction, then and we only 332 00:19:05,359 --> 00:19:08,200 Speaker 1: hear terrible things, and that's not going to get girls 333 00:19:08,200 --> 00:19:13,520 Speaker 1: and women involved in it. Um, But the visibility factor 334 00:19:13,960 --> 00:19:17,760 Speaker 1: is so much there for guys because the stereotype of 335 00:19:18,000 --> 00:19:21,239 Speaker 1: computers is nerds who we see on TV. In the 336 00:19:21,320 --> 00:19:24,960 Speaker 1: social network, we're seeing guys at the forefront and girls 337 00:19:25,080 --> 00:19:28,440 Speaker 1: sort of his set dressings every now and then. And um, 338 00:19:28,480 --> 00:19:30,480 Speaker 1: this is sort of a side note, but I was 339 00:19:30,520 --> 00:19:33,000 Speaker 1: reading about something called the c s I effect a 340 00:19:33,000 --> 00:19:36,840 Speaker 1: couple of days ago, which is how it's a theory 341 00:19:36,880 --> 00:19:39,200 Speaker 1: at least on how the popularity of shows like c 342 00:19:39,440 --> 00:19:42,600 Speaker 1: s I has led to a real world surge in 343 00:19:42,760 --> 00:19:46,680 Speaker 1: women in forensics. Of all the stem fields, women dominate 344 00:19:46,840 --> 00:19:49,800 Speaker 1: forensics across the board. And there was an article in 345 00:19:49,840 --> 00:19:53,520 Speaker 1: New York Times a couple of weeks ago asking whether 346 00:19:53,640 --> 00:19:56,159 Speaker 1: or not computer science could use its own c s 347 00:19:56,240 --> 00:20:00,280 Speaker 1: I effect to maybe see more like cool women doing 348 00:20:00,320 --> 00:20:04,440 Speaker 1: cool things with cool computers, so that girls have that 349 00:20:04,560 --> 00:20:08,240 Speaker 1: visibility to change that stereotype, move it away from the 350 00:20:08,280 --> 00:20:12,160 Speaker 1: pro grammar, which clearly is trying to overcorrect for the 351 00:20:12,200 --> 00:20:16,680 Speaker 1: former stereotype of the antisocial guy who can't leave his basement, 352 00:20:17,200 --> 00:20:23,000 Speaker 1: and bring more women into the pop cultural computer full yeah. 353 00:20:23,040 --> 00:20:24,800 Speaker 1: And and there are a few examples of that in 354 00:20:24,800 --> 00:20:27,159 Speaker 1: in pretty popular TV. I mean, um, the show the 355 00:20:27,160 --> 00:20:29,720 Speaker 1: show Bones has Angelo, who is an artist and a 356 00:20:29,760 --> 00:20:33,760 Speaker 1: computer scientist who you know, granted, is also incredibly pretty 357 00:20:34,080 --> 00:20:36,400 Speaker 1: and doing her thing like in mini skirts. But that's 358 00:20:36,560 --> 00:20:38,720 Speaker 1: I guess what you do on television when you're in 359 00:20:38,720 --> 00:20:40,880 Speaker 1: the sciences, you know, going going back to your hero. 360 00:20:41,040 --> 00:20:43,960 Speaker 1: We all, we all love you, Lieutenant and hera and 361 00:20:44,000 --> 00:20:46,240 Speaker 1: I think that right now, and this is a larger 362 00:20:46,240 --> 00:20:48,879 Speaker 1: problem with television. They'll have to be incredibly pretty and 363 00:20:49,000 --> 00:20:52,240 Speaker 1: kind of niche nerd at the same time. Like you 364 00:20:52,280 --> 00:20:55,760 Speaker 1: can't just be a girl who's on a computer. You 365 00:20:55,800 --> 00:20:59,600 Speaker 1: have to be a geek girl. And that's another separate 366 00:20:59,600 --> 00:21:01,679 Speaker 1: thing that I'm sure you guys have discussed on the 367 00:21:01,680 --> 00:21:06,160 Speaker 1: show at some point we have not done episode. Well, 368 00:21:06,200 --> 00:21:08,240 Speaker 1: I almost don't even want to touch it because it's 369 00:21:08,280 --> 00:21:12,320 Speaker 1: so it's so tricky and button Yeah. Yeah, but I 370 00:21:12,359 --> 00:21:15,920 Speaker 1: feel like even in the real world, for the visible 371 00:21:15,960 --> 00:21:20,359 Speaker 1: mentors that we see, the Marrissa Myers and Ryl Sandberg's, etcetera, 372 00:21:21,240 --> 00:21:24,720 Speaker 1: it's still a very constructed and careful image that they 373 00:21:24,760 --> 00:21:27,560 Speaker 1: have to maintain. Oh yeah, it's such I feel like 374 00:21:27,600 --> 00:21:30,760 Speaker 1: it's insanely tricky territory that, you know, they have to 375 00:21:31,600 --> 00:21:34,880 Speaker 1: not downplay their femininity because that would be some kind 376 00:21:34,880 --> 00:21:39,080 Speaker 1: of crime against femaleness if they weren't somehow you know, 377 00:21:39,119 --> 00:21:41,960 Speaker 1: projecting that, but at the same time have to show 378 00:21:42,000 --> 00:21:44,919 Speaker 1: that they can be this power player and I don't know, 379 00:21:44,960 --> 00:21:47,840 Speaker 1: it's it's it's weird and terribly unfair. Yeah, it's almost 380 00:21:47,880 --> 00:21:49,640 Speaker 1: like they have to put on some sort of like 381 00:21:49,840 --> 00:21:54,879 Speaker 1: official feminine woman uniform to even enter the room, you know, 382 00:21:54,960 --> 00:21:58,639 Speaker 1: to to climb that tech ladder. It's like they won't 383 00:21:58,640 --> 00:22:01,639 Speaker 1: just accept. It's like you have to try so hard, 384 00:22:01,960 --> 00:22:05,280 Speaker 1: even harder than a man, maybe not only in your work, 385 00:22:05,840 --> 00:22:08,040 Speaker 1: but in your appearance and your wardrobe, in the things 386 00:22:08,080 --> 00:22:10,359 Speaker 1: you say and how you say them right well, and 387 00:22:10,440 --> 00:22:12,760 Speaker 1: on top of that too, there's this added burden not 388 00:22:12,840 --> 00:22:16,879 Speaker 1: only of the appearance, but also I have some sympathy 389 00:22:17,160 --> 00:22:20,080 Speaker 1: for these incredibly successful women who really don't need my 390 00:22:20,080 --> 00:22:23,840 Speaker 1: sympathy at all, but for the fact that since women 391 00:22:23,840 --> 00:22:28,040 Speaker 1: in tech is such a high profile issue right now, 392 00:22:28,440 --> 00:22:30,919 Speaker 1: I feel like they're almost bearing up the mantle of 393 00:22:31,040 --> 00:22:34,199 Speaker 1: all women and girls in tech and maybe even STEM 394 00:22:34,320 --> 00:22:37,440 Speaker 1: in general. So and I feel like with Marissa Meyer 395 00:22:37,520 --> 00:22:42,280 Speaker 1: in particular, the Yahoo CEO, that she's very uncomfortable with that, 396 00:22:42,280 --> 00:22:44,159 Speaker 1: that she just wants to do her job and do 397 00:22:44,200 --> 00:22:46,399 Speaker 1: it well. Yeah, she says all the time, like like, 398 00:22:46,480 --> 00:22:48,879 Speaker 1: I'm not a girl geek. I'm a geek. Can we 399 00:22:48,920 --> 00:22:51,840 Speaker 1: stop asking me questions about women and me giving birth 400 00:22:51,880 --> 00:22:54,200 Speaker 1: because that's not my job. Yeah. But at the same 401 00:22:54,240 --> 00:22:57,320 Speaker 1: time though, too, it's like, well, Marissa Meyer, you're coming 402 00:22:57,400 --> 00:23:01,119 Speaker 1: up at this time, and I feel a sympathy, But 403 00:23:01,119 --> 00:23:06,000 Speaker 1: at the same time it's with great power comes great responsibility. Yeah, 404 00:23:06,560 --> 00:23:09,920 Speaker 1: She's She's also said that she hasn't personally felt discriminated 405 00:23:09,960 --> 00:23:13,359 Speaker 1: against as a woman in that sector, and she's certainly 406 00:23:13,400 --> 00:23:15,879 Speaker 1: not the only person saying that. Um I had one word, 407 00:23:15,960 --> 00:23:17,800 Speaker 1: small story Before we move on to some of these 408 00:23:17,800 --> 00:23:20,720 Speaker 1: other really big names in tech leadership. UM. There was 409 00:23:20,760 --> 00:23:22,720 Speaker 1: a really great blog post that came out just this 410 00:23:22,760 --> 00:23:28,119 Speaker 1: month October by a programmer named Meredith Patterson who was 411 00:23:28,160 --> 00:23:30,840 Speaker 1: talking about how she's never experienced this this new girls 412 00:23:30,880 --> 00:23:34,720 Speaker 1: allowed kind of vibe in programming, and also talks about 413 00:23:34,760 --> 00:23:36,560 Speaker 1: how that may be due to the fact that she 414 00:23:36,640 --> 00:23:39,920 Speaker 1: has autism and and that that's you know, a separate 415 00:23:40,040 --> 00:23:44,080 Speaker 1: but related personality factor of of not always cluing in 416 00:23:44,160 --> 00:23:47,960 Speaker 1: on subtle social circumstances. UM. But you know that she 417 00:23:48,040 --> 00:23:50,760 Speaker 1: hates the assumption that all women have a bad experience 418 00:23:50,760 --> 00:23:53,520 Speaker 1: in tech because that's not her experience. I think that overall, 419 00:23:53,600 --> 00:23:57,320 Speaker 1: painting it as completely terrible or completely okay is is 420 00:23:57,359 --> 00:24:01,280 Speaker 1: harmful either way. Yeah, and there also stories on the 421 00:24:01,320 --> 00:24:06,040 Speaker 1: smaller scale too of women who might have been dissatisfied 422 00:24:06,040 --> 00:24:08,920 Speaker 1: with what was going on, but they're the exciting thing 423 00:24:08,920 --> 00:24:10,919 Speaker 1: about being in tech, And I feel as even as 424 00:24:10,960 --> 00:24:15,440 Speaker 1: someone who is not coding necessarily but involved in new 425 00:24:15,480 --> 00:24:19,280 Speaker 1: media UM, is that the field is so open for 426 00:24:19,320 --> 00:24:22,520 Speaker 1: you to make what you want to make. Kind of 427 00:24:22,560 --> 00:24:26,280 Speaker 1: in the instance of the women who founded skill Crush, Caroline, 428 00:24:26,280 --> 00:24:29,480 Speaker 1: I know that you've taken some skill Crush courses I did, 429 00:24:29,520 --> 00:24:32,359 Speaker 1: and it was actually a fantastic experience coming. You know, 430 00:24:32,440 --> 00:24:35,200 Speaker 1: I had no I had no experience in this area 431 00:24:35,280 --> 00:24:39,200 Speaker 1: at all, and so taking skill Crush classes it's like 432 00:24:39,359 --> 00:24:42,800 Speaker 1: such a you feel like you've walked into your best 433 00:24:42,840 --> 00:24:45,680 Speaker 1: friend's apartment and all of your mutual friends are over 434 00:24:45,800 --> 00:24:49,480 Speaker 1: and you're just like, hey, ladies, ladies on a couch 435 00:24:49,520 --> 00:24:52,679 Speaker 1: with beer. Perfect. No, but it's great because um, and 436 00:24:52,880 --> 00:24:54,280 Speaker 1: you know, not that I'm trying to make this into 437 00:24:54,280 --> 00:24:58,040 Speaker 1: an advertisement for skill Crush, you know, but the it's 438 00:24:58,119 --> 00:25:01,040 Speaker 1: like it's so obvious from the minute you sign up, 439 00:25:01,440 --> 00:25:04,639 Speaker 1: the minute you pay them that you have a complete 440 00:25:04,800 --> 00:25:08,959 Speaker 1: and total, open and honest and thoughtful support group of 441 00:25:09,000 --> 00:25:11,640 Speaker 1: women and some guys. There's some guys taking those classes. 442 00:25:11,960 --> 00:25:15,440 Speaker 1: But um, I just feel like that's so important because 443 00:25:15,600 --> 00:25:18,440 Speaker 1: I there there were some some weeks where it all tough, 444 00:25:18,800 --> 00:25:21,600 Speaker 1: got little tough, there were some tough lessons, and I 445 00:25:21,840 --> 00:25:24,040 Speaker 1: think I would have just said to heck with it 446 00:25:24,480 --> 00:25:27,600 Speaker 1: if there hadn't been those awesome women. Because the woman 447 00:25:27,600 --> 00:25:31,080 Speaker 1: who founded it, Ada or Adam, I'm sorry, Uh, you 448 00:25:31,119 --> 00:25:34,119 Speaker 1: know she's she's in the videos teaching you how to code. 449 00:25:34,520 --> 00:25:38,360 Speaker 1: She is this brainiac behind all of this coding stuff, 450 00:25:38,480 --> 00:25:42,200 Speaker 1: and there she is in her like cute little outfit 451 00:25:42,480 --> 00:25:45,280 Speaker 1: and you know, rocking some code and and so it was. 452 00:25:45,359 --> 00:25:48,800 Speaker 1: It was really comforting and kind of awesome. And it's 453 00:25:48,880 --> 00:25:54,080 Speaker 1: specifically designed right to teach women in particular code, not 454 00:25:54,160 --> 00:25:58,000 Speaker 1: that they exclude dudes, but it's geared specifically towards the 455 00:25:58,080 --> 00:26:00,960 Speaker 1: young professional woman who thinks that she's too busy to 456 00:26:01,040 --> 00:26:04,040 Speaker 1: learn how to code, but she can. That's amazing. And 457 00:26:04,160 --> 00:26:06,800 Speaker 1: especially yeah, I mean, you know, just having having those 458 00:26:06,880 --> 00:26:09,960 Speaker 1: role models was so important. Yeah, it's so important for 459 00:26:10,720 --> 00:26:15,520 Speaker 1: at least striving towards some kind of parody too, so 460 00:26:15,600 --> 00:26:18,200 Speaker 1: that we have more than Maurice and me Er Cheryl 461 00:26:18,240 --> 00:26:22,639 Speaker 1: Sandberg and some other exceptions to the rule to talk about. 462 00:26:22,760 --> 00:26:26,960 Speaker 1: Because even at the top top tiers of US companies, 463 00:26:27,000 --> 00:26:31,160 Speaker 1: at least, the numbers of women in leadership roles tech 464 00:26:31,200 --> 00:26:35,080 Speaker 1: wise are also down in accordance with the numbers that 465 00:26:35,119 --> 00:26:39,040 Speaker 1: are down for women who are even studying computer science. Yeah, 466 00:26:39,080 --> 00:26:42,359 Speaker 1: if we look at UH chief information officers, that's down 467 00:26:42,440 --> 00:26:46,280 Speaker 1: to nine percent in from twelve percent in two. It 468 00:26:46,280 --> 00:26:49,600 Speaker 1: seems to be pretty steadily declining from just a few 469 00:26:49,960 --> 00:26:56,000 Speaker 1: to even fewer and one poll, thirty of four hundred 470 00:26:56,000 --> 00:26:59,280 Speaker 1: and fifty American tech executives said that their I T 471 00:26:59,480 --> 00:27:07,120 Speaker 1: groups have no women in management agreed that women were underrepresented. Yeah, 472 00:27:07,200 --> 00:27:10,280 Speaker 1: and Tara Hunt, who's the CEO of a company called Biosphere, 473 00:27:10,359 --> 00:27:13,240 Speaker 1: told Forbes that the more women we see in high 474 00:27:13,320 --> 00:27:16,960 Speaker 1: profile technical roles at these companies, the more young women 475 00:27:17,000 --> 00:27:20,520 Speaker 1: will be inspired to pursue a career in technology. And 476 00:27:20,640 --> 00:27:24,119 Speaker 1: that visibility issue is something that comes up over and 477 00:27:24,200 --> 00:27:27,880 Speaker 1: over in not just tech, but in other stem fields 478 00:27:27,920 --> 00:27:30,960 Speaker 1: because I do think I think all of our experiences 479 00:27:31,000 --> 00:27:35,080 Speaker 1: speak to the power of actually seeing or in our 480 00:27:35,119 --> 00:27:39,720 Speaker 1: cases maybe not so much seeing women in these kinds 481 00:27:39,800 --> 00:27:43,560 Speaker 1: of jobs. And another kind of factor in why we 482 00:27:43,600 --> 00:27:46,720 Speaker 1: need more women in this field is because the very 483 00:27:46,760 --> 00:27:50,720 Speaker 1: simple concept of like hiring like um, if we look 484 00:27:50,840 --> 00:27:56,560 Speaker 1: at venture capital firms people giving funding to startups, venture 485 00:27:56,600 --> 00:28:01,080 Speaker 1: capitalists are men, and so you know, I'm not saying 486 00:28:01,080 --> 00:28:05,159 Speaker 1: that a man won't fund a woman's venture or vice versa. 487 00:28:05,280 --> 00:28:08,920 Speaker 1: But what I'm saying is that inherent biases do exist. 488 00:28:09,400 --> 00:28:11,640 Speaker 1: And so if it is this old boys club and 489 00:28:11,840 --> 00:28:15,720 Speaker 1: men are helping their their bros out, you know, maybe 490 00:28:15,720 --> 00:28:17,639 Speaker 1: it's time to get some more women in these fields 491 00:28:17,680 --> 00:28:19,720 Speaker 1: to help each other and and to help men too. 492 00:28:19,960 --> 00:28:22,400 Speaker 1: I'm not trying to exclude anything, sure, yeah, but yeah, 493 00:28:22,440 --> 00:28:24,520 Speaker 1: but it's still that kind of that thing that started 494 00:28:24,520 --> 00:28:27,199 Speaker 1: way back in the in the sixties of of you know, 495 00:28:27,400 --> 00:28:29,800 Speaker 1: being kind of driven by these connections that men make 496 00:28:29,840 --> 00:28:32,840 Speaker 1: in these professional organizations that women can't get into. Yeah, 497 00:28:32,840 --> 00:28:35,520 Speaker 1: there's after hours networking, and then there's also two when 498 00:28:35,560 --> 00:28:38,600 Speaker 1: it comes to venture capital and startups, this factor of 499 00:28:38,880 --> 00:28:41,040 Speaker 1: risk taking. I think this was something Caroline we talked 500 00:28:41,040 --> 00:28:44,000 Speaker 1: about in our episode on Instagram where those guys were 501 00:28:44,200 --> 00:28:47,120 Speaker 1: so successful because a lot of times it's a hallmark 502 00:28:47,400 --> 00:28:50,440 Speaker 1: of dudes who are really into startup where they're also 503 00:28:50,560 --> 00:28:54,520 Speaker 1: really into risk taking and you're probably gonna try a 504 00:28:54,520 --> 00:28:57,800 Speaker 1: lot of different things and you're moving a lot of 505 00:28:57,840 --> 00:29:02,040 Speaker 1: money around. And I think that women can also help 506 00:29:02,080 --> 00:29:08,360 Speaker 1: themselves too by being braver to take on those risks. 507 00:29:08,560 --> 00:29:11,680 Speaker 1: And speaking of venture capital, and this is a bit tangential, 508 00:29:11,760 --> 00:29:14,680 Speaker 1: but not too long ago, there was a huge internet 509 00:29:14,720 --> 00:29:17,720 Speaker 1: fallout when the guy who found a dead spin whose 510 00:29:17,800 --> 00:29:22,120 Speaker 1: name I am forgetting right now, announced very publicly that 511 00:29:22,160 --> 00:29:24,920 Speaker 1: he had raised five million dollars in venture capital to 512 00:29:25,040 --> 00:29:29,040 Speaker 1: start a website for women called Bustle dot com, oh 513 00:29:29,280 --> 00:29:32,720 Speaker 1: that whole thing. Yeah, And he was so proud of himself, 514 00:29:32,760 --> 00:29:35,000 Speaker 1: and he was obviously a business guy, and I think 515 00:29:35,040 --> 00:29:38,000 Speaker 1: that he was earnest and really wanting to start this 516 00:29:38,720 --> 00:29:43,720 Speaker 1: and everyone, every woman I think he was contributed to 517 00:29:43,760 --> 00:29:47,720 Speaker 1: any kind of women's media threw up her hands. But 518 00:29:47,880 --> 00:29:49,400 Speaker 1: he whin got the money though. At the end of 519 00:29:49,400 --> 00:29:52,280 Speaker 1: the day, he went and got the money. And that 520 00:29:52,320 --> 00:29:55,280 Speaker 1: means that there is five million dollars plus out there 521 00:29:55,920 --> 00:29:58,560 Speaker 1: looking for a female audience. Why did it have to 522 00:29:58,600 --> 00:30:01,760 Speaker 1: be a guy who sold the idea, lady or I 523 00:30:01,760 --> 00:30:03,400 Speaker 1: think all three of us just have our hands up 524 00:30:03,440 --> 00:30:06,320 Speaker 1: with each other because I could use some five million bucks. 525 00:30:06,360 --> 00:30:08,360 Speaker 1: I don't know about you, Yeah, but I wonder though 526 00:30:08,400 --> 00:30:11,560 Speaker 1: if he was I mean, definitely his business legacy with 527 00:30:12,280 --> 00:30:16,200 Speaker 1: dead Spin, which became very profitable, gave him credibility for 528 00:30:16,360 --> 00:30:18,840 Speaker 1: seeking out this venture capital. But when you look at 529 00:30:18,880 --> 00:30:21,680 Speaker 1: who funded it, it was a lot of other guys. 530 00:30:22,280 --> 00:30:25,320 Speaker 1: And so you do wonder though, that if all three 531 00:30:25,400 --> 00:30:29,800 Speaker 1: of us went to go pitch our lady fantastic web empire, 532 00:30:30,360 --> 00:30:33,080 Speaker 1: if we would have as much success. I just wonder 533 00:30:33,960 --> 00:30:36,600 Speaker 1: because it would be harder to find a woman to 534 00:30:36,640 --> 00:30:39,520 Speaker 1: fund us. You know, yeah, we we'd have to start 535 00:30:39,600 --> 00:30:42,920 Speaker 1: out by making this million dollar business and then but 536 00:30:43,080 --> 00:30:44,560 Speaker 1: how do you how do you do that when you're 537 00:30:44,560 --> 00:30:46,400 Speaker 1: a lady in a field that doesn't really want you 538 00:30:46,440 --> 00:30:49,200 Speaker 1: to exactly, well, we're going to start a PayPal account. 539 00:30:50,480 --> 00:30:53,160 Speaker 1: But again, that's why it is good that we do 540 00:30:53,280 --> 00:30:58,080 Speaker 1: have the Cheryl Sandberg's, the Marissa Myers is paving the 541 00:30:58,120 --> 00:31:02,360 Speaker 1: way who are for an now maybe taking on the mantle. 542 00:31:02,520 --> 00:31:05,360 Speaker 1: Although you know, they do have to deal with a 543 00:31:05,400 --> 00:31:09,560 Speaker 1: lot of criticism, but the good news is is that 544 00:31:09,600 --> 00:31:14,560 Speaker 1: it isn't always focused on their gender. Right. Yeah, you'll 545 00:31:14,800 --> 00:31:17,840 Speaker 1: see Mers and Myer catching flak when she poses in 546 00:31:18,160 --> 00:31:21,080 Speaker 1: what magazine was It was at Vogue? It was Vogue? Yeah, um, 547 00:31:21,160 --> 00:31:23,440 Speaker 1: kind of kind of sexy it up, um, as though 548 00:31:23,520 --> 00:31:26,400 Speaker 1: she cannot be a CEO and also a woman who 549 00:31:26,520 --> 00:31:30,040 Speaker 1: is a sexy lady. Um. But I think most of 550 00:31:30,040 --> 00:31:35,080 Speaker 1: the criticism against you know, Cheryl Sandberg or uh Jenny Rometti, 551 00:31:35,160 --> 00:31:38,320 Speaker 1: who's the CEO of IBM, people like that is more 552 00:31:38,400 --> 00:31:43,320 Speaker 1: that they're mismanaging their companies in specific ways that aren't gendered. 553 00:31:43,320 --> 00:31:45,640 Speaker 1: It's kind of comforting to me that people can criticize 554 00:31:45,680 --> 00:31:48,400 Speaker 1: these women as CEOs and not have to mention the 555 00:31:48,440 --> 00:31:51,400 Speaker 1: fact that ps they have lady bits in every single 556 00:31:51,440 --> 00:31:54,000 Speaker 1: news article. I mean, you know, they always say woman 557 00:31:54,400 --> 00:31:56,880 Speaker 1: and ceo and well, you know, but that's the way 558 00:31:56,880 --> 00:32:00,480 Speaker 1: that our language is structured. So it's kind of okay. Um, 559 00:32:00,640 --> 00:32:05,560 Speaker 1: specifically with Cheryl Sandberg, I was really comforted when criticism 560 00:32:05,560 --> 00:32:07,840 Speaker 1: of Lenin came in and a lot of it was 561 00:32:07,880 --> 00:32:11,520 Speaker 1: about classism instead of sexism. I thought that was I mean, 562 00:32:11,520 --> 00:32:14,120 Speaker 1: it's still it's terrible to classism like that was happening. 563 00:32:14,520 --> 00:32:16,680 Speaker 1: But I was like, well, this is a wonderful world 564 00:32:16,720 --> 00:32:18,480 Speaker 1: we're living in. No one is saying like, shut that 565 00:32:18,560 --> 00:32:20,719 Speaker 1: lady up. They were saying like shut that rich lady up, 566 00:32:20,720 --> 00:32:23,520 Speaker 1: which is a whole different issue exactly. I mean, just 567 00:32:23,560 --> 00:32:28,560 Speaker 1: the fact that she has the liberty to talk about 568 00:32:28,920 --> 00:32:32,040 Speaker 1: the ins and outs of Facebook and Google too, of 569 00:32:32,280 --> 00:32:35,040 Speaker 1: how it was, how it was for her sort of 570 00:32:35,120 --> 00:32:37,080 Speaker 1: learning the ropes as a woman, as a wife, as 571 00:32:37,120 --> 00:32:40,480 Speaker 1: a mother in there. I think you're right. Definitely speaks 572 00:32:40,480 --> 00:32:44,840 Speaker 1: to um maybe some progress that's being made. And I 573 00:32:44,920 --> 00:32:48,920 Speaker 1: noticed with Apple's recent higher of the former Barberry CEO 574 00:32:49,080 --> 00:32:55,640 Speaker 1: Angela Lawrence, seemed controversy free, which was surprising to see 575 00:32:55,920 --> 00:32:59,600 Speaker 1: in tech, but especially for women in tech getting big jobs. 576 00:32:59,680 --> 00:33:03,360 Speaker 1: But I do wonder if it went so smoothly because 577 00:33:03,520 --> 00:33:06,240 Speaker 1: she was hired on to oversee both online and in 578 00:33:06,320 --> 00:33:10,520 Speaker 1: store retail, and which is a more lady business exactly 579 00:33:10,560 --> 00:33:14,600 Speaker 1: because ladies shop. You guys be shopping. We love shopping exactly, 580 00:33:14,760 --> 00:33:19,000 Speaker 1: all of us choose, shoose, choose period and maybe a 581 00:33:19,080 --> 00:33:23,960 Speaker 1: Berbery trench, Oh I wish. But yeah, she she became 582 00:33:24,480 --> 00:33:29,000 Speaker 1: the one woman among Apple's top ten senior executives. So 583 00:33:29,160 --> 00:33:33,200 Speaker 1: at least she is there. And you know, you do 584 00:33:33,320 --> 00:33:35,920 Speaker 1: have the case of like Julie Larson Green, who was 585 00:33:35,960 --> 00:33:39,160 Speaker 1: recently hired on to be the head of Xbox. You know, 586 00:33:39,200 --> 00:33:41,480 Speaker 1: she's in charge of all of the hardware, the games, 587 00:33:41,520 --> 00:33:43,960 Speaker 1: the music, the entertainment. And some of the comments that 588 00:33:44,000 --> 00:33:48,960 Speaker 1: were being made just they're not good human comments to 589 00:33:49,000 --> 00:33:52,840 Speaker 1: make about humans. Well we haven't even gotten into gaming. Yeah, 590 00:33:52,960 --> 00:33:55,200 Speaker 1: gaming is a whole separate issue. Back when we were 591 00:33:55,200 --> 00:33:58,800 Speaker 1: talking numbers of programmers, and they were pretty dire, you know, 592 00:33:58,920 --> 00:34:02,760 Speaker 1: like down into the into the teens, three of programmers 593 00:34:02,760 --> 00:34:06,239 Speaker 1: of games or women and and and they and they 594 00:34:06,280 --> 00:34:08,560 Speaker 1: make you know, like ten thousand dollars less on average 595 00:34:08,560 --> 00:34:11,400 Speaker 1: than their male counterparts, which I think is a higher 596 00:34:11,440 --> 00:34:15,760 Speaker 1: percentage than than another in other industries, which is ludicrous 597 00:34:15,760 --> 00:34:18,600 Speaker 1: and ridiculous until you start looking at the kind of 598 00:34:19,080 --> 00:34:23,280 Speaker 1: games that that most developers are making, and the female 599 00:34:23,520 --> 00:34:28,000 Speaker 1: characters that most games include, and just the general gamer culture. 600 00:34:28,320 --> 00:34:30,080 Speaker 1: If you guys want me to come back and rant 601 00:34:30,160 --> 00:34:33,319 Speaker 1: a lot about gaming culture, I can do that thing. Yeah, 602 00:34:33,360 --> 00:34:35,960 Speaker 1: you haven open invitation for sure, because I know that 603 00:34:36,000 --> 00:34:39,440 Speaker 1: we have a lot of gamers among our listeners and women, 604 00:34:39,440 --> 00:34:43,000 Speaker 1: and gaming has been a requested topic. So it's definitely 605 00:34:43,080 --> 00:34:45,600 Speaker 1: something we should spend an entire episode on because there's 606 00:34:45,640 --> 00:34:49,520 Speaker 1: so much there to talk about. It's a very pointy topic. 607 00:34:49,560 --> 00:34:51,520 Speaker 1: I'm kind of I just said that. Now I'm like, 608 00:34:51,719 --> 00:34:54,160 Speaker 1: maybe I can change my name and move to another 609 00:34:54,239 --> 00:34:56,480 Speaker 1: state and I will never have to do this. Well, 610 00:34:56,520 --> 00:35:00,640 Speaker 1: in the meantime, let's close things off on a positive note, 611 00:35:00,719 --> 00:35:03,640 Speaker 1: because we do have these women at the top, and 612 00:35:03,680 --> 00:35:07,840 Speaker 1: we also have women like those who founded skill Crush, 613 00:35:08,040 --> 00:35:12,680 Speaker 1: who are doing things with their skills to empower other 614 00:35:12,760 --> 00:35:15,879 Speaker 1: women and other girls who might not be quite yet 615 00:35:16,000 --> 00:35:18,800 Speaker 1: at the level of a Cheryl Sandberg. And that's really 616 00:35:18,800 --> 00:35:21,279 Speaker 1: cool and there's a lot of conversation that's going on. 617 00:35:21,320 --> 00:35:24,120 Speaker 1: There's a lot of mentorship happening, and a lot of 618 00:35:24,360 --> 00:35:29,279 Speaker 1: organizations that are starting up specifically to help fix this 619 00:35:29,400 --> 00:35:33,120 Speaker 1: pipeline problem. Right there's groups like Girls who Code and 620 00:35:33,239 --> 00:35:35,680 Speaker 1: Black Girls who Code. And I know we've talked a 621 00:35:35,719 --> 00:35:38,440 Speaker 1: lot about the US, but there is little Miss Geek 622 00:35:38,520 --> 00:35:42,440 Speaker 1: in the UK. They run school workshops getting women from 623 00:35:42,440 --> 00:35:45,360 Speaker 1: the industry to actually come into the schools and talk 624 00:35:45,440 --> 00:35:49,040 Speaker 1: to the girls, like, look at this human person who's 625 00:35:49,080 --> 00:35:51,799 Speaker 1: in front of you, who is successful and happy. You 626 00:35:51,840 --> 00:35:54,640 Speaker 1: could also be like her. So I mean that those 627 00:35:54,680 --> 00:35:57,440 Speaker 1: are just just three examples. And then on top of that, 628 00:35:57,480 --> 00:35:59,839 Speaker 1: there is it's more of a hacker kind of thing, 629 00:36:00,160 --> 00:36:03,480 Speaker 1: but there's Ada Fruit and fem engineer. And finally, we 630 00:36:03,600 --> 00:36:07,799 Speaker 1: referenced earlier the Anita Borg Institute for Women and Technology, 631 00:36:07,880 --> 00:36:12,040 Speaker 1: which began a competition recently for quote Top Company for 632 00:36:12,280 --> 00:36:16,160 Speaker 1: Technical Women Award, which was given in two thousand twelve 633 00:36:16,360 --> 00:36:21,480 Speaker 1: f y I to American Express. So cool. Yeah. Another 634 00:36:21,600 --> 00:36:23,920 Speaker 1: interesting note, the more College of Art, which is a 635 00:36:23,960 --> 00:36:27,279 Speaker 1: women's school, is offering starting this fall major in game 636 00:36:27,280 --> 00:36:30,440 Speaker 1: design and as of August seven, of like a hundred 637 00:36:30,480 --> 00:36:32,400 Speaker 1: and thirty incoming freshmen had said that they were going 638 00:36:32,440 --> 00:36:35,640 Speaker 1: to sign up for it. Awesome. Yeah, and we didn't 639 00:36:35,719 --> 00:36:39,880 Speaker 1: talk about salaries specifically. But girls, if you were listening 640 00:36:39,920 --> 00:36:42,440 Speaker 1: to this and you are considering a college major, I 641 00:36:42,520 --> 00:36:45,200 Speaker 1: really think about computer science because the median income is 642 00:36:45,280 --> 00:36:50,479 Speaker 1: eight dollars. Yeah. That's one of the reasons why there 643 00:36:50,520 --> 00:36:52,920 Speaker 1: has been this push too for women to get into 644 00:36:53,080 --> 00:36:57,720 Speaker 1: computer science because engineering and computer PSI out of the gate, 645 00:36:58,280 --> 00:37:03,239 Speaker 1: make so much more than the average job title. You mean, 646 00:37:03,640 --> 00:37:07,560 Speaker 1: more than a journalism major, Kristen. That's I can't even 647 00:37:07,600 --> 00:37:11,080 Speaker 1: imagine making more than a journalism major. Fact, you can 648 00:37:11,120 --> 00:37:15,800 Speaker 1: put vegetables in your raben, ladies. I was a newspapers major, 649 00:37:17,000 --> 00:37:19,920 Speaker 1: so we made even less. That's what you're saying. I 650 00:37:20,040 --> 00:37:22,560 Speaker 1: was creative writing, so I think I win this game. Actually, 651 00:37:23,960 --> 00:37:26,040 Speaker 1: high five to all of us. Let's go sign up 652 00:37:26,080 --> 00:37:29,840 Speaker 1: for some skill crush classes. And uh, Lauren, thanks so 653 00:37:29,960 --> 00:37:32,040 Speaker 1: much for coming on. Thank you so so much for 654 00:37:32,040 --> 00:37:33,800 Speaker 1: having me. This has been a blast. Well, you'll definitely 655 00:37:33,800 --> 00:37:37,600 Speaker 1: have to come back for a women in Gaming conversation 656 00:37:37,719 --> 00:37:41,600 Speaker 1: in the future. I would love and or hate to Well, 657 00:37:41,640 --> 00:37:44,000 Speaker 1: we want to hear from listeners out there are you 658 00:37:44,080 --> 00:37:50,080 Speaker 1: in computer science, Are you in any kind of coding, developing, programming, etcetera. 659 00:37:50,160 --> 00:37:52,120 Speaker 1: We want to hear from you. We do don't care 660 00:37:52,120 --> 00:37:53,920 Speaker 1: if you're a guy who crushes code or a girl 661 00:37:53,960 --> 00:37:56,839 Speaker 1: who crushes code. And send us your letters mom Stuff 662 00:37:56,840 --> 00:37:58,920 Speaker 1: at Discovery dot com or you can tweet us at 663 00:37:58,960 --> 00:38:01,920 Speaker 1: mom Stuff podcast or find us on Facebook. And we've 664 00:38:01,920 --> 00:38:04,759 Speaker 1: got a couple of letters to share with you. And 665 00:38:05,120 --> 00:38:09,560 Speaker 1: back to our letters. Well, I got an email here 666 00:38:09,640 --> 00:38:15,040 Speaker 1: from Justina on our episode on the Maestros of Classical Music. 667 00:38:15,440 --> 00:38:18,520 Speaker 1: She writes, thanks for your podcast on women conductors. One 668 00:38:18,520 --> 00:38:20,800 Speaker 1: thing you mentioned briefly was that the number of women 669 00:38:20,920 --> 00:38:24,200 Speaker 1: musicians in orchestras is on the rise, But do you 670 00:38:24,239 --> 00:38:27,359 Speaker 1: know why. A big part of the reason is the 671 00:38:27,400 --> 00:38:31,840 Speaker 1: implementation of blind audition in music schools, conservatories and orchestras. 672 00:38:31,960 --> 00:38:34,399 Speaker 1: It turns out that when judges judge on performance only 673 00:38:34,440 --> 00:38:36,640 Speaker 1: and have no knowledge of who it is that's playing, 674 00:38:37,040 --> 00:38:43,279 Speaker 1: more women are selected who now, So thanks Justina. I 675 00:38:43,480 --> 00:38:45,640 Speaker 1: have a letter here from a listener who would like 676 00:38:45,719 --> 00:38:48,840 Speaker 1: to remain anonymous, and she wanted to offer her thoughts 677 00:38:48,920 --> 00:38:52,359 Speaker 1: on women in the workforce. After Kristin and I did 678 00:38:52,400 --> 00:38:55,880 Speaker 1: our series on leanin um. She says, I started thinking 679 00:38:55,880 --> 00:38:59,360 Speaker 1: about my own profession and my experience as a professional woman. 680 00:39:00,000 --> 00:39:03,120 Speaker 1: In my company, behavior services for children and adults, there 681 00:39:03,120 --> 00:39:06,000 Speaker 1: are maybe six men. Seven if you count the maintenance man's, 682 00:39:06,360 --> 00:39:10,520 Speaker 1: they're about fifty women. In my department, behavior therapists, there 683 00:39:10,520 --> 00:39:13,319 Speaker 1: are no men. Are clinical director as a woman, my 684 00:39:13,400 --> 00:39:15,960 Speaker 1: supervisor as a woman. Even when I was in grad 685 00:39:15,960 --> 00:39:18,560 Speaker 1: school for clinical mental health counseling, there were few men. 686 00:39:18,920 --> 00:39:21,560 Speaker 1: Out of twenty people in my cohort, there were three. 687 00:39:22,000 --> 00:39:24,040 Speaker 1: There were more men as professors, but most of them 688 00:39:24,040 --> 00:39:27,480 Speaker 1: were in the academic field, not applied services. The work 689 00:39:27,600 --> 00:39:30,000 Speaker 1: environment is very different than any place I have worked 690 00:39:30,000 --> 00:39:33,640 Speaker 1: in before. Obviously, it is very female friendly. When planning 691 00:39:33,680 --> 00:39:36,400 Speaker 1: get together with co workers, we typically think girls nights 692 00:39:36,440 --> 00:39:39,560 Speaker 1: in or pampering night. We are also super helpful towards 693 00:39:39,560 --> 00:39:41,560 Speaker 1: each other. If one of us is stressed, someone will 694 00:39:41,600 --> 00:39:44,959 Speaker 1: give an encouraging word. It's really nice. On the other hand, 695 00:39:44,960 --> 00:39:47,120 Speaker 1: there can be times when the group can get collectively 696 00:39:47,120 --> 00:39:52,759 Speaker 1: wrapped up in cavetching sessions. I'll just say, when we distress, 697 00:39:52,880 --> 00:39:56,600 Speaker 1: we talk and often complain. We also don't do confrontation easily. 698 00:39:56,640 --> 00:39:58,480 Speaker 1: If there is an issue it takes a long time 699 00:39:58,560 --> 00:40:00,880 Speaker 1: for to get resolved. I haven't had a chance to 700 00:40:00,920 --> 00:40:03,040 Speaker 1: read Lean In, but most of your podcast seemed to 701 00:40:03,040 --> 00:40:05,160 Speaker 1: focus on fields where a man at the boss or 702 00:40:05,200 --> 00:40:07,839 Speaker 1: women have to compete or work with men. Have there 703 00:40:07,840 --> 00:40:10,840 Speaker 1: been any studies looking at all female workplaces? While it 704 00:40:11,000 --> 00:40:13,040 Speaker 1: is fun and listen intimidating to be in an all 705 00:40:13,040 --> 00:40:15,520 Speaker 1: woman workplace, it is nice every once in a while 706 00:40:15,560 --> 00:40:19,319 Speaker 1: to have a guy around. And I can tell you, 707 00:40:19,360 --> 00:40:24,880 Speaker 1: anonymous listener, I have worked in a majority female work 708 00:40:24,960 --> 00:40:29,840 Speaker 1: environment and I don't know that I've actually seen studies 709 00:40:29,880 --> 00:40:32,000 Speaker 1: about it though. Yeah, I don't know that I've seen 710 00:40:32,320 --> 00:40:37,040 Speaker 1: any studies either. Yeah, but other listeners, if you've worked 711 00:40:37,040 --> 00:40:40,080 Speaker 1: in an all female workplace in high school, I did 712 00:40:40,080 --> 00:40:43,720 Speaker 1: work in a daycare center and that was all women, 713 00:40:44,280 --> 00:40:47,560 Speaker 1: um and lots of babies. But but yeah, if other 714 00:40:47,600 --> 00:40:51,960 Speaker 1: listeners can relate, send us an email. Mom Stuff at 715 00:40:51,960 --> 00:40:54,000 Speaker 1: Discovery dot com is where you can send them. You 716 00:40:54,040 --> 00:40:56,600 Speaker 1: can also follow us on Twitter at mom Stuff Podcast 717 00:40:56,880 --> 00:40:59,959 Speaker 1: and find us on Facebook, and you can find Laura 718 00:41:00,000 --> 00:41:03,960 Speaker 1: and Voguel bomb Over at tech Stuff and Forward Thinking, 719 00:41:04,480 --> 00:41:07,600 Speaker 1: So definitely check out her podcast as well, and you 720 00:41:07,600 --> 00:41:10,680 Speaker 1: can also follow us on Instagram and stuff I've never 721 00:41:10,719 --> 00:41:13,400 Speaker 1: told you, and you should watch every single one of 722 00:41:13,400 --> 00:41:18,360 Speaker 1: our one hundred plus videos on YouTube. Yeah there are 723 00:41:18,400 --> 00:41:21,200 Speaker 1: a lot of them. YouTube. Dot com slash stuff I've 724 00:41:21,200 --> 00:41:27,960 Speaker 1: never told you, and don't forget to subscribe for more 725 00:41:28,000 --> 00:41:30,640 Speaker 1: on this and thousands of other topics. 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