1 00:00:03,600 --> 00:00:06,880 Speaker 1: Hello Sunshine, Hey fam Today, on the Bride Side, we're 2 00:00:06,920 --> 00:00:09,559 Speaker 1: asking the question when was the first time you saw 3 00:00:09,600 --> 00:00:13,480 Speaker 1: yourself truly represented on the screen. The President and the 4 00:00:13,520 --> 00:00:16,720 Speaker 1: CEO of the Geena Davis Institute, Madeline Dunono, is here 5 00:00:16,880 --> 00:00:19,840 Speaker 1: and we're unpacking some of the mind blowing studies the 6 00:00:19,840 --> 00:00:22,360 Speaker 1: Institute has done, like have you ever noticed how one 7 00:00:22,360 --> 00:00:25,360 Speaker 1: dimensional TV moms are. We'll also talk about how just 8 00:00:25,400 --> 00:00:29,440 Speaker 1: seeing a female president on TV actually shifts voter attitudes 9 00:00:29,520 --> 00:00:32,000 Speaker 1: off screen. It's Monday, August nineteenth. 10 00:00:32,120 --> 00:00:34,920 Speaker 2: I'm Simone Boyce, I'm Danielle Robe and this is the 11 00:00:34,960 --> 00:00:38,199 Speaker 2: Bride Side from Hello Sunshine, a daily show where we 12 00:00:38,240 --> 00:00:42,279 Speaker 2: come together to share women's stories, to laugh, learn and 13 00:00:42,440 --> 00:00:49,160 Speaker 2: brighten your day. Simon, Happy Monday, Happy Monday. Let's kick 14 00:00:49,200 --> 00:00:51,960 Speaker 2: things off with on My Mind Monday. It's our opportunity 15 00:00:52,000 --> 00:00:56,080 Speaker 2: to share stories that motivate us, inspire curiosity, provide a 16 00:00:56,120 --> 00:00:57,920 Speaker 2: fresh perspective for the week ahead. 17 00:00:57,960 --> 00:01:01,000 Speaker 3: You know, oh yeah, I'm so here this what's on 18 00:01:01,040 --> 00:01:01,800 Speaker 3: your mind today? 19 00:01:02,320 --> 00:01:05,400 Speaker 1: Well, it's a bit of a source object. Why. I'm 20 00:01:05,440 --> 00:01:06,640 Speaker 1: going to start with a tough question. 21 00:01:06,800 --> 00:01:08,680 Speaker 3: Okay, okay, you know I love a question. 22 00:01:08,800 --> 00:01:11,880 Speaker 1: Give it to me. How many times do you think 23 00:01:12,640 --> 00:01:14,880 Speaker 1: you've let your emotions run wild at work? 24 00:01:16,240 --> 00:01:17,240 Speaker 3: At work. 25 00:01:18,600 --> 00:01:22,399 Speaker 2: Over the course of the last eleven years, I'd say 26 00:01:22,920 --> 00:01:25,160 Speaker 2: probably like four that I regret. 27 00:01:25,520 --> 00:01:29,040 Speaker 1: Yeah, I think that's accurate for me too. I ask 28 00:01:29,200 --> 00:01:32,600 Speaker 1: because I recently came across this article on Business Insider 29 00:01:32,800 --> 00:01:35,360 Speaker 1: that's all about the benefits of learning how to control 30 00:01:35,400 --> 00:01:38,759 Speaker 1: your emotions at work. It's this side of personal development 31 00:01:38,800 --> 00:01:41,760 Speaker 1: that's not often talked about. Yea, and it's causing me 32 00:01:42,000 --> 00:01:44,640 Speaker 1: to ask myself, Okay, what would my career look like 33 00:01:44,760 --> 00:01:47,520 Speaker 1: if I managed my emotions better? So this piece is 34 00:01:47,560 --> 00:01:50,120 Speaker 1: written by a former sales director, a turned life coach 35 00:01:50,160 --> 00:01:53,200 Speaker 1: and advice columnist named Susie Moore, and in the piece, 36 00:01:53,360 --> 00:01:55,560 Speaker 1: more lays out the ways that we can manage our 37 00:01:55,560 --> 00:01:59,160 Speaker 1: emotions when we're faced with disappointment in our careers. And 38 00:01:59,200 --> 00:02:01,400 Speaker 1: one of the things that points out is that quote 39 00:02:01,440 --> 00:02:05,160 Speaker 1: business is simply a blend of math and emotions. She says, 40 00:02:05,200 --> 00:02:09,320 Speaker 1: money is easier to understand than our emotions. And emotions 41 00:02:09,480 --> 00:02:10,400 Speaker 1: are the messy part. 42 00:02:10,600 --> 00:02:15,440 Speaker 2: Ooh, emotions are the messy part, but they're also the evidence. 43 00:02:15,560 --> 00:02:19,560 Speaker 2: They're the signals that we need to read into. 44 00:02:19,600 --> 00:02:22,160 Speaker 1: I think, yeah, they're there. For a reason. I think 45 00:02:22,200 --> 00:02:24,280 Speaker 1: we need to listen to them. I think it's also 46 00:02:24,400 --> 00:02:27,480 Speaker 1: about learning to manage them and learning to control them 47 00:02:27,520 --> 00:02:30,400 Speaker 1: and channel them in the proper way in a business setting. Yeah, 48 00:02:30,440 --> 00:02:32,560 Speaker 1: so hear me out. So in this article, she explained 49 00:02:32,600 --> 00:02:36,560 Speaker 1: how she manages the most common business frustrations, like the 50 00:02:36,680 --> 00:02:39,600 Speaker 1: sting of rejection. For example, she says that her bounce 51 00:02:39,639 --> 00:02:42,240 Speaker 1: back rate after a rejection has gone up from two 52 00:02:42,360 --> 00:02:46,320 Speaker 1: days to just one to two minutes, which is pretty impressive. Interesting, 53 00:02:46,440 --> 00:02:48,480 Speaker 1: and she says she's able to do that by understanding 54 00:02:48,480 --> 00:02:51,440 Speaker 1: that rejection is inevitable and we should embrace it, we 55 00:02:51,480 --> 00:02:53,520 Speaker 1: should learn from it and use it to our advantage. 56 00:02:53,600 --> 00:02:55,600 Speaker 1: This kind of reminds me of the conversation that we 57 00:02:55,680 --> 00:02:59,200 Speaker 1: had with Jamie kern Lima, Like she talks about how 58 00:02:59,240 --> 00:03:02,760 Speaker 1: she received so many no's in the course of trying 59 00:03:02,800 --> 00:03:05,040 Speaker 1: to pitch her business right, and she learned to just 60 00:03:05,080 --> 00:03:06,320 Speaker 1: like let it bounce off of her. 61 00:03:07,000 --> 00:03:10,240 Speaker 3: Yeah, the bounce back rate is important. I agree with that. 62 00:03:11,040 --> 00:03:14,800 Speaker 2: And there's like a whole category of therapy called somatics 63 00:03:15,240 --> 00:03:17,800 Speaker 2: where people kind of teach you to breathe through things, 64 00:03:17,840 --> 00:03:19,520 Speaker 2: and they say you can breathe through emotions in like 65 00:03:19,600 --> 00:03:20,440 Speaker 2: ninety seconds. 66 00:03:20,639 --> 00:03:23,520 Speaker 1: That's really impressive. My bounce back rate, I think is 67 00:03:23,560 --> 00:03:26,480 Speaker 1: twenty four hours. I usually add, I give myself twenty 68 00:03:26,480 --> 00:03:30,160 Speaker 1: four hours to sit with the sting of rejection and 69 00:03:30,160 --> 00:03:31,760 Speaker 1: then I'm like, you got to move on. How about you? 70 00:03:31,800 --> 00:03:33,320 Speaker 1: What do you think your bounce back rate is? 71 00:03:33,560 --> 00:03:35,440 Speaker 2: It depends what the rejection is. If it hits a 72 00:03:35,520 --> 00:03:38,720 Speaker 2: childhood wound, it probably takes a little longer. Like the 73 00:03:38,800 --> 00:03:40,520 Speaker 2: last time I had a guy break up with me, 74 00:03:41,200 --> 00:03:43,160 Speaker 2: it didn't take twenty four hours. It took me like 75 00:03:43,160 --> 00:03:48,320 Speaker 2: two years. But in terms of work stuff, I totally agree. 76 00:03:48,360 --> 00:03:50,000 Speaker 2: I think twenty four hours is a great rule. 77 00:03:50,200 --> 00:03:53,080 Speaker 1: Okay, So the next one that Susie mentions is super important, 78 00:03:53,320 --> 00:03:55,160 Speaker 1: and this is something that I think a lot of 79 00:03:55,200 --> 00:03:58,080 Speaker 1: women facing their careers is pretty universal, which is how 80 00:03:58,120 --> 00:04:02,160 Speaker 1: to manage internetroal and negativity on the Internet. This is 81 00:04:02,440 --> 00:04:06,640 Speaker 1: particularly relevant for people who are in public facing careers, influencers, 82 00:04:06,720 --> 00:04:10,120 Speaker 1: creators online and She said that she used to feel 83 00:04:10,160 --> 00:04:13,040 Speaker 1: like an insensitive remark online would leave her crushed, but 84 00:04:13,120 --> 00:04:15,840 Speaker 1: she learned to not ticket personally. She says, when you 85 00:04:15,880 --> 00:04:18,240 Speaker 1: can understand that the only thing a mean remark can 86 00:04:18,320 --> 00:04:20,080 Speaker 1: do is make you sad for a few minutes, it 87 00:04:20,120 --> 00:04:23,960 Speaker 1: loses its power and its luster, and kind of in 88 00:04:24,000 --> 00:04:27,200 Speaker 1: the same way that she advises us to anticipate rejection 89 00:04:27,720 --> 00:04:30,599 Speaker 1: that it's inevitable, she also advises here that we start 90 00:04:30,600 --> 00:04:33,520 Speaker 1: to expect these kinds of negative comments instead of fearing them. 91 00:04:33,640 --> 00:04:35,240 Speaker 1: Do you think that'll work? Yeah? 92 00:04:35,520 --> 00:04:36,360 Speaker 3: I do like this. 93 00:04:36,440 --> 00:04:40,400 Speaker 2: I mean Hillary Clinton always says you take criticism seriously 94 00:04:40,440 --> 00:04:43,840 Speaker 2: and not personally, and I really like that quote. 95 00:04:44,720 --> 00:04:47,080 Speaker 3: I also think you have to consider the source. Where 96 00:04:47,160 --> 00:04:48,120 Speaker 3: is it coming from. 97 00:04:48,120 --> 00:04:51,320 Speaker 1: Yes, yes, Is it coming from the inner circle or 98 00:04:51,400 --> 00:04:53,960 Speaker 1: is it coming from people who don't actually know me 99 00:04:54,120 --> 00:04:54,840 Speaker 1: or what I want. 100 00:04:55,320 --> 00:04:59,920 Speaker 3: I've heard people talk about how no one that is. 101 00:05:00,120 --> 00:05:03,640 Speaker 2: Like doing less or trying less than you are is 102 00:05:03,640 --> 00:05:06,159 Speaker 2: ever criticizing, because if you're trying, you know how hard 103 00:05:06,200 --> 00:05:07,239 Speaker 2: it is to freaking try. 104 00:05:08,560 --> 00:05:11,000 Speaker 1: Here's how she wraps up the article. I think this 105 00:05:11,080 --> 00:05:13,400 Speaker 1: is really powerful, she says, quote, when we realize our 106 00:05:13,440 --> 00:05:16,640 Speaker 1: emotions are most often in the way of sound decision 107 00:05:16,680 --> 00:05:20,159 Speaker 1: making and forward strides, we see how in control we 108 00:05:20,320 --> 00:05:20,880 Speaker 1: actually are. 109 00:05:21,880 --> 00:05:26,720 Speaker 2: I really like this, and I feel like it's important 110 00:05:26,760 --> 00:05:31,440 Speaker 2: not to discard our emotions, but to recognize them, synthesize them, 111 00:05:31,640 --> 00:05:34,120 Speaker 2: and then act accordingly beautifully said. 112 00:05:34,480 --> 00:05:37,799 Speaker 1: You know, this article brought up some interesting points about 113 00:05:37,839 --> 00:05:41,320 Speaker 1: how we face disappointment in our careers. And today we 114 00:05:41,360 --> 00:05:42,839 Speaker 1: have a special guest who is at the helm of 115 00:05:42,839 --> 00:05:48,600 Speaker 1: an organization dedicated to inclusive representation in film, TV, advertising, 116 00:05:48,920 --> 00:05:51,120 Speaker 1: who I had the pleasure of interviewing recently. 117 00:05:51,480 --> 00:05:53,160 Speaker 3: I'm so excited to hear this one. 118 00:05:53,240 --> 00:05:57,240 Speaker 2: I wasn't here because I was out of town for work. 119 00:05:57,320 --> 00:06:00,479 Speaker 2: I stayed in this little airbnb and and I got 120 00:06:00,480 --> 00:06:02,839 Speaker 2: my butt back to the bright side. 121 00:06:03,440 --> 00:06:05,680 Speaker 3: And I'm really excited to learn more about this one. 122 00:06:05,760 --> 00:06:08,400 Speaker 1: I think you're gonna like this one. My colleague and friend, 123 00:06:08,400 --> 00:06:11,440 Speaker 1: Madeline Ganono is the president and CEO of the Gena 124 00:06:11,520 --> 00:06:14,920 Speaker 1: Davis Institute on Gender in Media. If you haven't heard 125 00:06:14,960 --> 00:06:18,120 Speaker 1: of it, it is a research based organization that champions 126 00:06:18,480 --> 00:06:22,400 Speaker 1: equitable representation in media. It was founded by actor and 127 00:06:22,520 --> 00:06:27,120 Speaker 1: activists Gena Davis of League of their Own Beetlejuice, Thelma 128 00:06:27,120 --> 00:06:29,719 Speaker 1: and Louise Fame, and she founded it in two thousand 129 00:06:29,720 --> 00:06:31,520 Speaker 1: and four because she was stunned by the lack of 130 00:06:31,520 --> 00:06:35,640 Speaker 1: female characters and diversity on screen. And we've made so 131 00:06:35,839 --> 00:06:38,839 Speaker 1: many strides, but it was really interesting to talk to 132 00:06:38,920 --> 00:06:42,800 Speaker 1: Madeline about where we still have room for improvement. The 133 00:06:42,839 --> 00:06:45,400 Speaker 1: mission of the Institute is to change the world, one 134 00:06:45,440 --> 00:06:47,719 Speaker 1: story at a time, and they do that via data 135 00:06:47,800 --> 00:06:51,839 Speaker 1: driven research and providing insights and tools to help leading 136 00:06:51,880 --> 00:06:54,880 Speaker 1: content creators and movie studios do better in terms of 137 00:06:54,920 --> 00:06:58,279 Speaker 1: inclusive content. Madeline is going to drop some truly eye 138 00:06:58,320 --> 00:07:01,479 Speaker 1: opening facts about representation and TV and film, how we 139 00:07:01,560 --> 00:07:04,360 Speaker 1: can be the change as consumers, Like what is our 140 00:07:04,480 --> 00:07:08,159 Speaker 1: role in all of this, and the possibility that we 141 00:07:08,240 --> 00:07:11,800 Speaker 1: discover when we challenge these long held stereotypes. It's all 142 00:07:11,840 --> 00:07:13,920 Speaker 1: really fascinating and I can't wait for y' all to 143 00:07:13,960 --> 00:07:14,240 Speaker 1: hear it. 144 00:07:14,880 --> 00:07:16,400 Speaker 3: I can't wait to hear it along with you. 145 00:07:16,760 --> 00:07:18,840 Speaker 1: So after the break, I'm getting into it all with 146 00:07:18,920 --> 00:07:33,520 Speaker 1: Madeline Dino. Stay with us, Madeline, Welcome to the bright Side. 147 00:07:33,680 --> 00:07:36,679 Speaker 1: I'm so thrilled to be here. I am so thrilled 148 00:07:36,680 --> 00:07:39,520 Speaker 1: to see you. I'm so thrilled to be sitting across 149 00:07:39,560 --> 00:07:42,880 Speaker 1: this table from you. I can remember the first time 150 00:07:43,320 --> 00:07:47,640 Speaker 1: that I really understood the importance of representation. It was 151 00:07:48,400 --> 00:07:51,680 Speaker 1: nineteen ninety seven and the Cinderella movie with Brandy and 152 00:07:51,720 --> 00:07:54,920 Speaker 1: Whitney Houston had just come out, and I was probably 153 00:07:54,960 --> 00:07:57,760 Speaker 1: about nine years old at the time, and my mom, 154 00:07:57,840 --> 00:08:01,000 Speaker 1: who's a black actor. She doesn't act anymore, but she 155 00:08:01,120 --> 00:08:04,400 Speaker 1: did for many years. She made the premiere of this 156 00:08:04,560 --> 00:08:08,200 Speaker 1: movie sound like such a big deal, and to be honest, 157 00:08:08,240 --> 00:08:11,240 Speaker 1: I didn't really understand why. I was like, cool, Okay, 158 00:08:11,240 --> 00:08:14,960 Speaker 1: it's a Disney princess, It's Cinderella, Whitney Houston, love Whitney Houston. 159 00:08:15,000 --> 00:08:18,160 Speaker 1: This is really exciting. So I finally asked her, like, 160 00:08:18,240 --> 00:08:21,160 Speaker 1: why is this such a big deal to you? And 161 00:08:21,840 --> 00:08:24,040 Speaker 1: I realized that she was in tears at that point, 162 00:08:24,160 --> 00:08:26,679 Speaker 1: and she said that she had never seen a black 163 00:08:26,720 --> 00:08:30,680 Speaker 1: Disney princess before, and for her as an actor to 164 00:08:30,720 --> 00:08:35,360 Speaker 1: see that representation, I think it just brought up like 165 00:08:35,800 --> 00:08:38,920 Speaker 1: this well of emotions, you know, a lot of complex emotions. 166 00:08:39,480 --> 00:08:41,320 Speaker 1: And that's what I want to talk to you about 167 00:08:41,360 --> 00:08:44,400 Speaker 1: today is the why behind all of this, the impact. 168 00:08:44,880 --> 00:08:48,200 Speaker 1: So how does fictional media shape reality when it comes 169 00:08:48,240 --> 00:08:50,520 Speaker 1: to how women are perceived in society. 170 00:08:51,160 --> 00:08:53,840 Speaker 4: What happens in the world to make believe has real 171 00:08:53,880 --> 00:08:57,360 Speaker 4: world impact. And we've actually seen that with some of 172 00:08:57,400 --> 00:09:01,559 Speaker 4: our studies that we've done in surveys. So for example, 173 00:09:01,720 --> 00:09:04,360 Speaker 4: going all the way back to when Gina played one 174 00:09:04,400 --> 00:09:08,360 Speaker 4: of the first female presidents on TV in Commander in Chief. 175 00:09:08,800 --> 00:09:13,560 Speaker 4: The Taylor Kaplin Group did a poll and basically a 176 00:09:13,640 --> 00:09:17,440 Speaker 4: seventy six percent of the population at that time was 177 00:09:17,440 --> 00:09:20,959 Speaker 4: familiar with the show, and fifty eight percent surveyed said 178 00:09:21,040 --> 00:09:25,120 Speaker 4: they would take a female candidate more seriously because they 179 00:09:25,160 --> 00:09:27,960 Speaker 4: saw Gina in that role nineteen times. And that number 180 00:09:28,040 --> 00:09:30,720 Speaker 4: went up to sixty nine percent when it came to 181 00:09:30,840 --> 00:09:34,479 Speaker 4: adults thirty five to forty four. And it was completely 182 00:09:34,520 --> 00:09:38,040 Speaker 4: bipartisan in terms of the survey. So that's just one 183 00:09:38,200 --> 00:09:43,040 Speaker 4: tiny example of the power of media to influence our culture. 184 00:09:43,360 --> 00:09:47,600 Speaker 1: Wow, that can't be underestimated, especially right now in a 185 00:09:47,679 --> 00:09:51,040 Speaker 1: year like this. So I want to get into how 186 00:09:51,400 --> 00:09:54,520 Speaker 1: the Gena Davis Institute really came to be. Obviously, we 187 00:09:54,559 --> 00:09:57,080 Speaker 1: all know Geena Davis for her work and TV and 188 00:09:57,160 --> 00:10:00,680 Speaker 1: film dozens of credits, Thelman, Louise I mean, so many 189 00:10:00,720 --> 00:10:03,680 Speaker 1: beloved projects. She said that she was actually inspired to 190 00:10:03,720 --> 00:10:07,040 Speaker 1: start the institute after and experience watching a movie with 191 00:10:07,120 --> 00:10:09,360 Speaker 1: her daughter. Would you share that story and how it 192 00:10:09,440 --> 00:10:12,000 Speaker 1: underscores the work that you do today? Absolutely. 193 00:10:12,120 --> 00:10:15,040 Speaker 4: First of all, Gina had a heightened awareness of how 194 00:10:15,160 --> 00:10:18,840 Speaker 4: women and girls would feel based on the roles, the 195 00:10:19,000 --> 00:10:22,280 Speaker 4: iconic roles. Even today, people still walk up to her 196 00:10:22,280 --> 00:10:25,600 Speaker 4: and say, I played baseball because of you from Alegi 197 00:10:25,640 --> 00:10:28,560 Speaker 4: their own. So imagine now you're a new mom, and 198 00:10:28,640 --> 00:10:32,080 Speaker 4: of course you're going to start watching content with your kid. 199 00:10:32,240 --> 00:10:35,400 Speaker 4: And her daughter was two at the time, and it 200 00:10:35,520 --> 00:10:41,360 Speaker 4: immediately struck Gina the disparity in not seeing female characters 201 00:10:41,400 --> 00:10:44,240 Speaker 4: in anything she was showing her daughter, and it was 202 00:10:44,280 --> 00:10:47,400 Speaker 4: so surprising to her that she started asking her friends, hey, 203 00:10:47,480 --> 00:10:49,760 Speaker 4: did you notice that there just wasn't a lot of 204 00:10:49,760 --> 00:10:53,079 Speaker 4: female characters, And her friends were like, Nope, didn't notice. 205 00:10:53,160 --> 00:10:55,920 Speaker 4: And then when she'd go on meetings for potential acting 206 00:10:56,040 --> 00:10:58,880 Speaker 4: jobs and projects, she would bring up the same thing 207 00:10:58,920 --> 00:11:02,160 Speaker 4: and they'd be like no, they said, no, that we 208 00:11:02,240 --> 00:11:04,679 Speaker 4: have that fix that's so important to us. And they 209 00:11:04,760 --> 00:11:08,120 Speaker 4: named one female character in the entirety of a movie 210 00:11:08,440 --> 00:11:12,080 Speaker 4: to think that they had gender equality fix because they 211 00:11:12,120 --> 00:11:16,800 Speaker 4: had one female character. So it was really about unconscious 212 00:11:16,880 --> 00:11:21,280 Speaker 4: bias and people not realizing it until Gena pointed it 213 00:11:21,320 --> 00:11:24,319 Speaker 4: out and obviously use the data to prove her point. 214 00:11:24,559 --> 00:11:24,760 Speaker 2: Well. 215 00:11:24,760 --> 00:11:27,880 Speaker 1: In addition to your mission of ending unconscious bias and 216 00:11:28,000 --> 00:11:31,800 Speaker 1: fostering inclusion in global media. There's one other thing that 217 00:11:31,840 --> 00:11:34,560 Speaker 1: you say a lot at the GENA. Davis Institute and Foundation, 218 00:11:34,679 --> 00:11:36,800 Speaker 1: and that is if you can see it, you can 219 00:11:36,880 --> 00:11:40,280 Speaker 1: be it. This idea that there is power and representation. 220 00:11:41,120 --> 00:11:43,560 Speaker 1: As the President and CEO of the GENA. Davis Foundation, 221 00:11:43,679 --> 00:11:45,920 Speaker 1: you know the film and TV landscape better than just 222 00:11:45,960 --> 00:11:49,160 Speaker 1: about anyone. So broad strokes tell us where are we 223 00:11:49,320 --> 00:11:53,080 Speaker 1: today when it comes to positive representation and media. 224 00:11:53,160 --> 00:11:55,960 Speaker 4: When it comes to there's different verticals that we look at, 225 00:11:56,040 --> 00:12:01,280 Speaker 4: global advertising, global TV, global film, global gaming, and our 226 00:12:01,280 --> 00:12:05,480 Speaker 4: Refrierer Children's Television report first, so there's kind of good 227 00:12:05,520 --> 00:12:09,400 Speaker 4: news and bad news. When it comes to programming that 228 00:12:09,600 --> 00:12:13,760 Speaker 4: children are watching. We're pretty much so at parody. When 229 00:12:13,760 --> 00:12:17,120 Speaker 4: it comes to new programming, the default is still a 230 00:12:17,160 --> 00:12:20,600 Speaker 4: little bit more male. What's very interesting is when you 231 00:12:20,600 --> 00:12:24,800 Speaker 4: look at animation, when you have a more human like 232 00:12:25,440 --> 00:12:29,360 Speaker 4: animated character, we're at gender parody. But when it's a 233 00:12:29,400 --> 00:12:34,880 Speaker 4: talking trade or a mushroom, the default is male. Yeah, 234 00:12:34,920 --> 00:12:40,640 Speaker 4: so there's just some disparity there. But I will say positively, 235 00:12:40,880 --> 00:12:43,520 Speaker 4: the best progress that we have seen when it is 236 00:12:43,559 --> 00:12:47,960 Speaker 4: when it comes to people of color. In terms of leads, 237 00:12:48,559 --> 00:12:51,400 Speaker 4: we saw it was like fifty three percent of the 238 00:12:51,480 --> 00:12:54,880 Speaker 4: leads on TV that kids are watching or people of color. 239 00:12:54,920 --> 00:12:58,280 Speaker 4: We've seen that in advertising. We've seen that in the 240 00:12:58,320 --> 00:13:02,080 Speaker 4: portrayal of women in STEM where a number of years 241 00:13:02,120 --> 00:13:03,119 Speaker 4: ago it was only. 242 00:13:02,880 --> 00:13:03,959 Speaker 1: Twenty nine percent. 243 00:13:04,200 --> 00:13:05,960 Speaker 4: Now it went up to forty two percent in our 244 00:13:06,040 --> 00:13:09,160 Speaker 4: latest studies. So we're really really happy when you think 245 00:13:09,200 --> 00:13:11,240 Speaker 4: about people of color being forty one percent of the 246 00:13:11,280 --> 00:13:15,360 Speaker 4: population in the US, that that is starting to show 247 00:13:15,440 --> 00:13:16,600 Speaker 4: up on screen. 248 00:13:17,080 --> 00:13:20,440 Speaker 1: The name GENA. Davis Institute is so well respected in 249 00:13:20,480 --> 00:13:23,080 Speaker 1: this industry, but I imagine it must have been hard 250 00:13:23,080 --> 00:13:27,120 Speaker 1: to gain credibility or to go out and really pursue 251 00:13:27,120 --> 00:13:29,040 Speaker 1: this mission and get people on board. What was the 252 00:13:29,120 --> 00:13:33,400 Speaker 1: experience like getting it from the concept to becoming this 253 00:13:33,600 --> 00:13:34,760 Speaker 1: leader in research? 254 00:13:35,480 --> 00:13:39,320 Speaker 4: It really goes back to Gina's vision and her approach 255 00:13:39,360 --> 00:13:43,120 Speaker 4: and operating dynamics. So first of all, it was about 256 00:13:43,160 --> 00:13:46,080 Speaker 4: the data, and data has been the key to her access. 257 00:13:46,160 --> 00:13:49,800 Speaker 4: It's not our opinion, it's the data. And when you 258 00:13:49,840 --> 00:13:54,480 Speaker 4: approach people who are running billion dollar entities and you 259 00:13:54,600 --> 00:13:58,240 Speaker 4: approach them with the facts, they look at data all 260 00:13:58,320 --> 00:14:03,000 Speaker 4: day long. So number one, Number two, we're in the industry, 261 00:14:03,600 --> 00:14:07,120 Speaker 4: and it was really about being presented in a colleisial way. 262 00:14:07,200 --> 00:14:10,880 Speaker 4: We see the industry as partners and we know they 263 00:14:10,920 --> 00:14:13,600 Speaker 4: want to do good things. We're providing with the tools 264 00:14:13,640 --> 00:14:17,160 Speaker 4: and resources, so we never had a carrot and stick approach. 265 00:14:17,240 --> 00:14:20,480 Speaker 4: We never shame and blame, and we've always been more 266 00:14:20,520 --> 00:14:22,960 Speaker 4: of a B to B so a business to business 267 00:14:23,040 --> 00:14:28,200 Speaker 4: versus targeting consumers, and all of that together has been 268 00:14:28,280 --> 00:14:29,680 Speaker 4: the key to our success. 269 00:14:30,040 --> 00:14:31,720 Speaker 1: I want to drill down into the data with you 270 00:14:31,800 --> 00:14:35,400 Speaker 1: because the data are truly remarkable. One study looked at 271 00:14:35,440 --> 00:14:40,239 Speaker 1: the representation of families, specifically how scripted TV moms are represented. 272 00:14:40,280 --> 00:14:42,240 Speaker 1: So here are some of the highlights from that research. 273 00:14:42,720 --> 00:14:45,840 Speaker 1: Eight out of ten moms were slender, seventy percent of 274 00:14:45,880 --> 00:14:48,840 Speaker 1: moms take on the domestic tasks. If there is a 275 00:14:48,880 --> 00:14:51,680 Speaker 1: clear breadwinner, nine out of ten times it's a dad. 276 00:14:52,520 --> 00:14:54,920 Speaker 1: And this last one threw me for a loop. Less 277 00:14:54,960 --> 00:14:58,240 Speaker 1: than one in ten TV parents had a messy house. 278 00:14:58,920 --> 00:15:02,160 Speaker 1: How unrealistic is that? Will you talk about the impact 279 00:15:02,320 --> 00:15:04,560 Speaker 1: of these unrealistic standards on women. 280 00:15:05,360 --> 00:15:07,960 Speaker 4: It's shocking because it is the twenty first century, But 281 00:15:08,120 --> 00:15:11,680 Speaker 4: we found out that for the most part, TV moms 282 00:15:11,720 --> 00:15:17,200 Speaker 4: today are still mostly white, they're young, they're thin, they're 283 00:15:17,240 --> 00:15:20,840 Speaker 4: not disabled, they're not career et cetera, et cetera, and 284 00:15:20,880 --> 00:15:24,080 Speaker 4: that was really surprising. We've always looked at the intersection 285 00:15:24,280 --> 00:15:28,080 Speaker 4: of gender and race and lgbtqia and disability, et cetera. 286 00:15:28,680 --> 00:15:32,160 Speaker 4: The body diversity came up a number of years ago 287 00:15:32,240 --> 00:15:37,000 Speaker 4: because in looking at the population, over forty percent of 288 00:15:37,000 --> 00:15:41,800 Speaker 4: our population is of a larger body type, and so 289 00:15:42,000 --> 00:15:45,960 Speaker 4: body diversity became very, very important for us, and it's 290 00:15:46,000 --> 00:15:50,280 Speaker 4: something that we've been including in our data for a 291 00:15:50,360 --> 00:15:54,240 Speaker 4: number of years across global advertising, global TV, like all 292 00:15:54,280 --> 00:15:58,440 Speaker 4: of our studies, because not everybody's a size zero and 293 00:15:58,480 --> 00:16:02,240 Speaker 4: there's a lot of discriminate. So, for example, we have 294 00:16:02,440 --> 00:16:06,720 Speaker 4: found that characters that are of a larger body type 295 00:16:07,040 --> 00:16:10,200 Speaker 4: are the brunt of the joke. They're always seen as 296 00:16:10,440 --> 00:16:13,760 Speaker 4: you know, eating or out of breath, and just terrible, 297 00:16:13,880 --> 00:16:15,440 Speaker 4: terrible stereotypes. 298 00:16:15,720 --> 00:16:18,800 Speaker 1: I want to talk about female directors. We had Stephanie 299 00:16:18,800 --> 00:16:21,080 Speaker 1: Allen and Tick Nataro on our show recently to talk 300 00:16:21,120 --> 00:16:23,640 Speaker 1: about a film they co directed called am I Okay. 301 00:16:24,080 --> 00:16:26,320 Speaker 1: In just a few weeks, we have Zoe Kravitz making 302 00:16:26,360 --> 00:16:30,520 Speaker 1: her directorial debut with Blink Twice, Scarlett Johansson getting behind 303 00:16:30,560 --> 00:16:33,640 Speaker 1: the Helm with Eleanor the Great. But I was shocked 304 00:16:33,640 --> 00:16:36,000 Speaker 1: to see that in the ninety six year history of 305 00:16:36,040 --> 00:16:39,239 Speaker 1: the Academy Awards, just nine women have ever been nominated 306 00:16:39,280 --> 00:16:41,840 Speaker 1: for Best Directors since nineteen twenty eight, and a woman 307 00:16:41,840 --> 00:16:45,480 Speaker 1: has only won three times. I mean, this makes me 308 00:16:45,560 --> 00:16:48,760 Speaker 1: think of the whole controversy around Greta Gerwig not being 309 00:16:48,800 --> 00:16:53,200 Speaker 1: nominated for Barbie last year. Why is this number still 310 00:16:53,240 --> 00:16:53,680 Speaker 1: so low? 311 00:16:53,960 --> 00:16:56,920 Speaker 4: One thing is the pipeline, and there are many other 312 00:16:57,240 --> 00:17:01,760 Speaker 4: wonderful organizations that study what's happening behind the camera. We 313 00:17:01,840 --> 00:17:04,520 Speaker 4: don't really focus behind the camera. But first of all, 314 00:17:04,640 --> 00:17:08,800 Speaker 4: it's a pipeline problem. So if, for example, it's a 315 00:17:09,240 --> 00:17:12,120 Speaker 4: three to one five to one ratio of male directors 316 00:17:12,119 --> 00:17:15,919 Speaker 4: to female directors, it's about the opportunity. And we know 317 00:17:16,000 --> 00:17:20,480 Speaker 4: that female directors don't have the same opportunities and access 318 00:17:20,480 --> 00:17:24,600 Speaker 4: to capital and that pathway to getting their films made. 319 00:17:24,880 --> 00:17:27,840 Speaker 4: Most female directors can get say one film made, even 320 00:17:27,880 --> 00:17:30,200 Speaker 4: if they max out their credit cards, but to get 321 00:17:30,240 --> 00:17:33,640 Speaker 4: that second and that third film made is very, very challenging. 322 00:17:33,720 --> 00:17:37,920 Speaker 4: So it's really important that female directors can find their 323 00:17:38,000 --> 00:17:41,880 Speaker 4: way into the pipeline in order for us to have 324 00:17:42,000 --> 00:17:46,480 Speaker 4: more female directed movies, and when you look at independent film, 325 00:17:46,920 --> 00:17:50,600 Speaker 4: you have gender parity or even more for female directors. 326 00:17:50,840 --> 00:17:53,439 Speaker 4: But we need it with these bigger budget films. So 327 00:17:53,560 --> 00:17:57,320 Speaker 4: historically female directors are relegated more to the indie that 328 00:17:57,560 --> 00:18:02,480 Speaker 4: lower production budget and that's where the pipeline needs to 329 00:18:02,520 --> 00:18:04,880 Speaker 4: be filled. And hopefully with a lot of the programs 330 00:18:05,359 --> 00:18:08,880 Speaker 4: that the studios have, that'll start to change. But it's 331 00:18:08,920 --> 00:18:11,479 Speaker 4: been glacial. I mean, the film industry is over one 332 00:18:11,520 --> 00:18:12,359 Speaker 4: hundred years old. 333 00:18:12,600 --> 00:18:15,240 Speaker 1: So you have a number of tools that you use 334 00:18:15,560 --> 00:18:19,399 Speaker 1: whenever you gather this data and then you take it 335 00:18:19,440 --> 00:18:22,600 Speaker 1: out to meet with the studios to share it with them, 336 00:18:22,640 --> 00:18:24,600 Speaker 1: to share it with these decision makers who have the 337 00:18:24,640 --> 00:18:28,600 Speaker 1: potential to create real progress. Walk us through some of 338 00:18:28,600 --> 00:18:30,160 Speaker 1: those tools that you've created. 339 00:18:30,880 --> 00:18:35,439 Speaker 4: So we have always provided kind of a benchmark for 340 00:18:35,560 --> 00:18:39,320 Speaker 4: how are we doing. And we have had the privilege 341 00:18:39,320 --> 00:18:43,560 Speaker 4: of working with Google and usc of a Tribe School 342 00:18:43,600 --> 00:18:48,920 Speaker 4: of Engineering to incorporate some machine learning, some AI along 343 00:18:48,960 --> 00:18:51,840 Speaker 4: with a lot of human expert coding, which is led 344 00:18:51,880 --> 00:18:55,440 Speaker 4: by doctor Meredith Conroy who leads are research and insights team. 345 00:18:55,960 --> 00:19:00,320 Speaker 4: We have wonderful PhDs in house on our team who 346 00:19:00,400 --> 00:19:03,480 Speaker 4: conduct the research. And so one of those tools is 347 00:19:03,520 --> 00:19:07,040 Speaker 4: GDIQ Gena Davis Inclusion quot and that is really a 348 00:19:07,080 --> 00:19:11,000 Speaker 4: benchmarking tool that gives us a sense of where are 349 00:19:11,040 --> 00:19:16,600 Speaker 4: we in terms of gender, race, ethnicity, LGBTQAA, age, fifty plus, disability, 350 00:19:16,600 --> 00:19:19,199 Speaker 4: body type, which are the six core dimensions. 351 00:19:19,960 --> 00:19:21,640 Speaker 1: But that's after the fact. 352 00:19:22,000 --> 00:19:24,359 Speaker 4: So one of the tools that we developed with our 353 00:19:24,400 --> 00:19:28,640 Speaker 4: partners at usc Viterbi is called spell Check for Bias, 354 00:19:28,840 --> 00:19:33,840 Speaker 4: and it's actually looking at scripts and we're breaking down 355 00:19:33,840 --> 00:19:37,080 Speaker 4: a script with a different lens. We're looking at all 356 00:19:37,119 --> 00:19:41,320 Speaker 4: the characters that are speaking and we pull them off 357 00:19:41,480 --> 00:19:44,439 Speaker 4: into a data set and then we walk it through. 358 00:19:44,920 --> 00:19:48,280 Speaker 4: You know, are those characters that are contributing dialogue? Are 359 00:19:48,359 --> 00:19:51,600 Speaker 4: they female, are they LGBTQA, what are they? Is there 360 00:19:51,640 --> 00:19:55,600 Speaker 4: an opportunity? And it's really about not attacking a story, 361 00:19:56,080 --> 00:19:59,400 Speaker 4: not telling the storytellers what to do, not invading their 362 00:19:59,400 --> 00:20:02,560 Speaker 4: authentic t but it's pointing out opportunities. And then we'll 363 00:20:02,600 --> 00:20:08,560 Speaker 4: also look at things like sexism, racism, and essentially we 364 00:20:08,640 --> 00:20:11,880 Speaker 4: provide all the backup in the data for our partners 365 00:20:11,960 --> 00:20:14,760 Speaker 4: so that if they are evaluating a script, whether it's 366 00:20:14,760 --> 00:20:18,040 Speaker 4: for green light, before it goes to casting, they have 367 00:20:18,080 --> 00:20:21,159 Speaker 4: a different lens on looking at it because most leaders 368 00:20:21,160 --> 00:20:22,879 Speaker 4: are looking at the top of the call sheet. 369 00:20:23,200 --> 00:20:24,400 Speaker 1: They're not looking at. 370 00:20:24,280 --> 00:20:29,800 Speaker 4: Every single person in the cast that could be contributing dialogue. 371 00:20:29,840 --> 00:20:35,480 Speaker 4: And that's a great way to organically infuse more inclusion 372 00:20:35,880 --> 00:20:39,200 Speaker 4: and diversity in a script before it gets made. 373 00:20:39,480 --> 00:20:44,400 Speaker 1: So when you uncover that research, what happens next? How 374 00:20:44,400 --> 00:20:47,400 Speaker 1: do we implement these findings into actual change. 375 00:20:47,960 --> 00:20:50,840 Speaker 4: So there's a cycle of change for us. So the 376 00:20:50,880 --> 00:20:54,480 Speaker 4: first thing we do is we usually socialize the data 377 00:20:54,800 --> 00:20:58,280 Speaker 4: very publicly through an event, all the social media, all 378 00:20:58,280 --> 00:21:02,040 Speaker 4: the emails, etc. The real work starts when we start 379 00:21:02,080 --> 00:21:06,240 Speaker 4: taking it out studio by studio, network by network, streamer 380 00:21:06,240 --> 00:21:11,440 Speaker 4: by streamer. We do hundreds and hundreds of meetings and presentations. 381 00:21:12,240 --> 00:21:14,800 Speaker 4: It could be a year or so, is a full circle, 382 00:21:15,280 --> 00:21:18,280 Speaker 4: and then we'll measure the industry again. And that's how 383 00:21:18,280 --> 00:21:21,960 Speaker 4: we're able to make change. We're able to collect whether 384 00:21:22,000 --> 00:21:27,480 Speaker 4: it's anecdotal or we'll receive direct testimonials from changes that 385 00:21:27,520 --> 00:21:30,440 Speaker 4: people have made. That's how the real work gets done. 386 00:21:30,680 --> 00:21:32,320 Speaker 1: We need to take a quick break, but we'll be 387 00:21:32,400 --> 00:21:34,960 Speaker 1: right back with more from the president and CEO of 388 00:21:35,000 --> 00:21:38,280 Speaker 1: the Geena Davis Institute on Gender in Media, Madeline Gnoo, 389 00:21:38,600 --> 00:21:48,480 Speaker 1: stay with us and we're back with Madeline Danoo. You 390 00:21:48,520 --> 00:21:51,919 Speaker 1: actually have some interesting recommendations for the creatives that you 391 00:21:52,000 --> 00:21:54,440 Speaker 1: meet with, and I want to discuss that more now. 392 00:21:54,560 --> 00:21:58,199 Speaker 1: One of the suggestions is to make diversity explicit on 393 00:21:58,280 --> 00:22:00,399 Speaker 1: the page. Can you give me an exam of what 394 00:22:00,400 --> 00:22:02,639 Speaker 1: that would look like? Absolutely? 395 00:22:03,040 --> 00:22:06,880 Speaker 4: For example, if there is going to be a person 396 00:22:06,920 --> 00:22:10,800 Speaker 4: of color, what culture are they from? Do they have 397 00:22:10,880 --> 00:22:14,199 Speaker 4: a sense of agency? Do they have a job. So, 398 00:22:14,320 --> 00:22:18,440 Speaker 4: for example, we have seen a lot of stereotypes when 399 00:22:18,480 --> 00:22:21,480 Speaker 4: there is a person of color, they're relegated to, say 400 00:22:21,480 --> 00:22:25,080 Speaker 4: a service role. They're not shown as being a leader, 401 00:22:25,359 --> 00:22:29,919 Speaker 4: an entrepreneur. And it's those kinds of nuances that we 402 00:22:29,960 --> 00:22:32,439 Speaker 4: can provide a lot of opportunities for people to just 403 00:22:33,119 --> 00:22:35,800 Speaker 4: rethink that. And it has to be in the script 404 00:22:35,920 --> 00:22:39,000 Speaker 4: because if it's not in the script, then you're leaving 405 00:22:39,160 --> 00:22:42,240 Speaker 4: a lot up for question. And particularly when it comes 406 00:22:42,280 --> 00:22:46,479 Speaker 4: to say secondary characters, a lot of those decisions are 407 00:22:46,520 --> 00:22:49,600 Speaker 4: being made by say a second ad on set. They're 408 00:22:49,640 --> 00:22:52,920 Speaker 4: not being made by the in house casting people or 409 00:22:52,960 --> 00:22:56,040 Speaker 4: even a casting agency. So there's so many different touch 410 00:22:56,080 --> 00:23:00,320 Speaker 4: points on how actors wind up being in a scene 411 00:23:00,560 --> 00:23:02,919 Speaker 4: and being cast for that. So it's a matter of 412 00:23:02,960 --> 00:23:06,760 Speaker 4: looking at all of those opportunities. 413 00:23:07,280 --> 00:23:09,280 Speaker 1: Madeline, I know you personally, and I know that you're 414 00:23:09,320 --> 00:23:11,840 Speaker 1: always out there giving talks and promoting the work that 415 00:23:11,880 --> 00:23:14,680 Speaker 1: you're doing at the Geena Davis Institute. You must meet 416 00:23:14,680 --> 00:23:17,280 Speaker 1: so many creatives at these events. I'm curious if any 417 00:23:17,320 --> 00:23:20,239 Speaker 1: of them have shared any success stories with you, or 418 00:23:20,600 --> 00:23:23,680 Speaker 1: stories of how your work has impacted their storytelling. 419 00:23:24,280 --> 00:23:27,879 Speaker 4: So one of our Border directors, Wendy Calhoun, who's a 420 00:23:28,320 --> 00:23:33,679 Speaker 4: fabulous showrunner writer. Many years ago, she was working on 421 00:23:34,720 --> 00:23:40,119 Speaker 4: season two of Empire, and there was a discussion in 422 00:23:40,160 --> 00:23:45,600 Speaker 4: the writer's room about a venture capitalist and the character 423 00:23:45,880 --> 00:23:49,560 Speaker 4: was going to be male, and Wendy was very versed 424 00:23:49,800 --> 00:23:52,480 Speaker 4: in the work of the Institute. This is before she 425 00:23:52,560 --> 00:23:55,960 Speaker 4: joined her Border Directors, and she researched and found binders 426 00:23:55,960 --> 00:23:58,560 Speaker 4: full of female venture capitalists and went back into the 427 00:23:58,600 --> 00:24:01,879 Speaker 4: writer's room and pitched it. And that character was played 428 00:24:01,880 --> 00:24:06,400 Speaker 4: by Marissa Tomey for season two. So it was that simple. 429 00:24:06,480 --> 00:24:08,439 Speaker 4: I mean, it's a key stroke. I mean that's just 430 00:24:08,480 --> 00:24:10,800 Speaker 4: one example, but we have a lot of examples like that. 431 00:24:11,280 --> 00:24:13,840 Speaker 1: I'm sure you do. Because the GENA. Davis Institute is 432 00:24:13,880 --> 00:24:18,840 Speaker 1: celebrating twenty years this year. Huge congratulations on that and 433 00:24:18,920 --> 00:24:20,840 Speaker 1: just all the impact that you've been a part of. 434 00:24:21,440 --> 00:24:25,000 Speaker 1: You were there, You joined the company five years after 435 00:24:25,600 --> 00:24:26,399 Speaker 1: its creation. 436 00:24:26,840 --> 00:24:30,160 Speaker 4: That's true, Gina invited me, and I've had the privilege 437 00:24:30,160 --> 00:24:32,800 Speaker 4: of leading the organization for the last fifteen years. 438 00:24:32,960 --> 00:24:35,520 Speaker 1: So what is the accomplishment you're most proud of there? 439 00:24:36,200 --> 00:24:40,720 Speaker 4: I would say the data speaks for itself, knowing that 440 00:24:40,840 --> 00:24:44,240 Speaker 4: our theory of change has worked. What we saw before 441 00:24:44,240 --> 00:24:48,040 Speaker 4: the pandemic for the first time ever in the data 442 00:24:48,440 --> 00:24:51,080 Speaker 4: was being able to achieve gender parody for female lead 443 00:24:51,160 --> 00:24:57,440 Speaker 4: characters and the most watched most popular children's television programming, 444 00:24:58,080 --> 00:25:01,320 Speaker 4: followed by the following year achieving gender parity for female 445 00:25:01,400 --> 00:25:05,040 Speaker 4: leads in the largest grossing films out of the US, 446 00:25:05,240 --> 00:25:09,520 Speaker 4: and then also followed by achieving gender parity actually fifty 447 00:25:09,520 --> 00:25:13,879 Speaker 4: two percent for secondary characters in children's television. Now, this 448 00:25:14,119 --> 00:25:17,600 Speaker 4: was all pre pandemic, and you have to take into 449 00:25:17,640 --> 00:25:23,119 Speaker 4: consideration the impact of the strikes and the pandemic on 450 00:25:23,280 --> 00:25:27,879 Speaker 4: TV production, creatives, etc. You can't put these reports out 451 00:25:28,359 --> 00:25:32,520 Speaker 4: without taking the market and the situation that we're in 452 00:25:32,520 --> 00:25:34,840 Speaker 4: into account is going to take a number of years. 453 00:25:35,240 --> 00:25:36,200 Speaker 1: It's a new normal. 454 00:25:36,720 --> 00:25:39,840 Speaker 4: But we're just happy that even with our recent studies 455 00:25:39,880 --> 00:25:43,040 Speaker 4: and TV, we've still been able to see those parody numbers. 456 00:25:43,960 --> 00:25:47,280 Speaker 1: Why are you optimistic about the future of film and TV? 457 00:25:47,760 --> 00:25:53,240 Speaker 4: It's really with the response and how the industry has 458 00:25:53,280 --> 00:25:56,919 Speaker 4: not only embraced our data, but has embraced data. And 459 00:25:57,160 --> 00:26:02,920 Speaker 4: also it requires these companies be organizationally ready, no matter 460 00:26:02,960 --> 00:26:06,360 Speaker 4: how willing they are, and they have all built infrastructures 461 00:26:07,200 --> 00:26:12,479 Speaker 4: with departments with very seasoned executives whose sole job it 462 00:26:12,680 --> 00:26:16,640 Speaker 4: is is to look at the content through this lens. 463 00:26:16,960 --> 00:26:20,639 Speaker 4: Whereas decades ago, years ago, when we first started, you 464 00:26:20,680 --> 00:26:23,800 Speaker 4: didn't have that kind of empowerment. You didn't have those 465 00:26:23,880 --> 00:26:28,439 Speaker 4: kind of executives with those roles being empowered to review 466 00:26:28,520 --> 00:26:33,320 Speaker 4: content and to point out various things. So the structure 467 00:26:33,359 --> 00:26:36,640 Speaker 4: has changed and that allows for the companies to move 468 00:26:36,680 --> 00:26:38,000 Speaker 4: forward and also progress. 469 00:26:38,960 --> 00:26:41,960 Speaker 1: What is our role as consumers and all of this. 470 00:26:42,640 --> 00:26:47,879 Speaker 4: Well, we're all debating on what streaming services we want. 471 00:26:48,359 --> 00:26:52,960 Speaker 4: We all have a voice, and the industry pays attention 472 00:26:53,200 --> 00:26:56,960 Speaker 4: to what people say and what people post and their tiktoks, 473 00:26:57,000 --> 00:27:00,880 Speaker 4: et cetera. They watch it, and so you have a voice, 474 00:27:01,280 --> 00:27:04,840 Speaker 4: whereas decades ago you didn't. And there's also the power 475 00:27:04,880 --> 00:27:09,720 Speaker 4: of the wallet as well to support movies and TV 476 00:27:09,840 --> 00:27:12,600 Speaker 4: shows that you believe reflect the stories that you want 477 00:27:12,640 --> 00:27:13,080 Speaker 4: to see. 478 00:27:13,720 --> 00:27:15,879 Speaker 1: You know that statistic that you shared at the beginning 479 00:27:15,920 --> 00:27:19,000 Speaker 1: of our conversation about just the having the image of 480 00:27:19,000 --> 00:27:21,919 Speaker 1: a female president in a TV show or a film 481 00:27:22,520 --> 00:27:26,520 Speaker 1: can greatly impact the perception of female leadership in reality. 482 00:27:26,960 --> 00:27:29,520 Speaker 1: And I think about the fact that we have Kamala 483 00:27:29,520 --> 00:27:31,480 Speaker 1: Harris running. I think about the fact that she would 484 00:27:31,480 --> 00:27:34,600 Speaker 1: be our first female president, and I'm curious how you 485 00:27:34,760 --> 00:27:37,479 Speaker 1: think that would shift the paradigm when it comes to 486 00:27:37,920 --> 00:27:40,880 Speaker 1: how presidents are represented on TV screens. 487 00:27:41,560 --> 00:27:43,919 Speaker 4: I'm not going to tell the story correctly, but Gina 488 00:27:43,960 --> 00:27:47,399 Speaker 4: has a great story. One of the leaders of I 489 00:27:47,440 --> 00:27:51,000 Speaker 4: think it was Finland, they had only had a female president, 490 00:27:51,560 --> 00:27:55,600 Speaker 4: and there was a little boy who actually asked, can 491 00:27:55,680 --> 00:27:59,560 Speaker 4: boys be president? Well, because in that country? And I 492 00:27:59,640 --> 00:28:02,840 Speaker 4: know I'm mess this story up, so forgive me, uh. 493 00:28:02,880 --> 00:28:04,919 Speaker 1: No, but we get it, you know what I mean? Yeah, So, 494 00:28:05,040 --> 00:28:08,480 Speaker 1: I mean that's the opportunity. You know. I had a 495 00:28:08,480 --> 00:28:10,760 Speaker 1: similar moment when I was watching the Olympics with my 496 00:28:10,840 --> 00:28:13,560 Speaker 1: son the other day. We had been watching a lot 497 00:28:13,600 --> 00:28:17,159 Speaker 1: of the women's events, and I didn't even really realize 498 00:28:17,160 --> 00:28:19,399 Speaker 1: that that we hadn't been watching the men's events, and 499 00:28:19,440 --> 00:28:22,280 Speaker 1: so I turned on the Olympics and it happened to 500 00:28:22,280 --> 00:28:25,320 Speaker 1: be men's swimming and he was like, Mommy, I want 501 00:28:25,359 --> 00:28:29,280 Speaker 1: to watch the girls. Where are the girls? I was like, yes, yes, 502 00:28:29,400 --> 00:28:32,359 Speaker 1: I did something right, Yes, you've tried him well exactly. 503 00:28:33,560 --> 00:28:36,520 Speaker 1: Is that an area that the Geena Davis Institute might 504 00:28:36,560 --> 00:28:38,440 Speaker 1: explore in the future is well. 505 00:28:38,440 --> 00:28:41,600 Speaker 4: We have had the opportunity to do a few little 506 00:28:41,840 --> 00:28:45,880 Speaker 4: sports studies and one came out of a personal experience 507 00:28:45,880 --> 00:28:48,040 Speaker 4: from Gina. Because some of you may or may not 508 00:28:48,240 --> 00:28:54,240 Speaker 4: know that Gina uh qualified and pursued the Olympic archery team. 509 00:28:54,400 --> 00:28:58,080 Speaker 1: Wait, I did not know this. Yes she was, yes, yes. 510 00:28:58,040 --> 00:29:02,360 Speaker 4: So after seeing it was the Australian Olympics, she thought 511 00:29:02,760 --> 00:29:06,400 Speaker 4: archery was just so beautiful and it was so measurable, 512 00:29:07,200 --> 00:29:11,240 Speaker 4: and so she for two and a half years trained 513 00:29:12,200 --> 00:29:14,560 Speaker 4: and qualified for the Olympic trials. 514 00:29:15,000 --> 00:29:17,000 Speaker 1: She didn't make the team, okay, but she did do 515 00:29:17,160 --> 00:29:18,680 Speaker 1: very very well. 516 00:29:18,720 --> 00:29:22,240 Speaker 4: And what's interesting is a number of years ago her 517 00:29:22,360 --> 00:29:26,400 Speaker 4: archery coach called her and said, I was looking at 518 00:29:26,400 --> 00:29:31,920 Speaker 4: the statistics for National Association of American Archery and girls 519 00:29:32,000 --> 00:29:35,480 Speaker 4: participation in archery went up one hundred and five percent 520 00:29:36,240 --> 00:29:37,680 Speaker 4: in the year twenty twelve. 521 00:29:39,000 --> 00:29:43,560 Speaker 1: Why two movies? Two movies? Can you name them? Yes, 522 00:29:43,640 --> 00:29:46,760 Speaker 1: Brave and Hunger Games. 523 00:29:46,880 --> 00:29:47,120 Speaker 2: Yes. 524 00:29:47,640 --> 00:29:50,560 Speaker 1: I remember actually covering this when I was working as 525 00:29:50,600 --> 00:29:53,920 Speaker 1: a reporter and we leaned on your insights. Yes, from 526 00:29:53,960 --> 00:29:55,240 Speaker 1: the Geena Davis Institute. 527 00:29:55,360 --> 00:29:58,800 Speaker 4: They watched the movie they bought a bow. It was instantaneous. 528 00:29:58,840 --> 00:30:05,800 Speaker 4: So imagine after seeing all these fabulous female Team USA athletes, 529 00:30:05,600 --> 00:30:10,640 Speaker 4: You'm just thrilled to see fencing and wrestling and seeing 530 00:30:10,680 --> 00:30:12,880 Speaker 4: girls participation go up in those sports. 531 00:30:13,520 --> 00:30:18,560 Speaker 1: There again is the real life impact of all these initiatives. 532 00:30:19,360 --> 00:30:21,960 Speaker 1: Thank you so much for coming on the bright side. Madeline, 533 00:30:21,960 --> 00:30:29,840 Speaker 1: thrilled you brought the brightness. Madeline Denono is the president 534 00:30:29,840 --> 00:30:33,160 Speaker 1: and CEO of the GENA. Davis Institute on Gender in Media. 535 00:30:33,280 --> 00:30:35,760 Speaker 1: You can check out their research on women's representation and 536 00:30:35,840 --> 00:30:38,760 Speaker 1: media on their website Genadavisinstitute dot org. 537 00:30:44,040 --> 00:30:45,200 Speaker 3: That's it for today's show. 538 00:30:45,400 --> 00:30:50,040 Speaker 2: Tomorrow, boxing World champion and actor Kaylee Reese, She's here 539 00:30:50,080 --> 00:30:53,760 Speaker 2: to talk about making history as an Indigenous actor thanks 540 00:30:53,840 --> 00:30:57,440 Speaker 2: to our partners at Airbnb. Listen and follow the bright 541 00:30:57,480 --> 00:31:00,800 Speaker 2: Side on the iHeartRadio app, Apple Podcasts, or wherever you 542 00:31:00,840 --> 00:31:01,880 Speaker 2: get your podcasts. 543 00:31:02,080 --> 00:31:04,720 Speaker 1: I'm Simone Boye. You can find me at Simone Voice 544 00:31:04,880 --> 00:31:06,440 Speaker 1: on Instagram and TikTok. 545 00:31:06,800 --> 00:31:09,560 Speaker 2: I'm Danielle Robe on Instagram and TikTok. 546 00:31:09,680 --> 00:31:10,840 Speaker 3: That's r O b A. 547 00:31:11,080 --> 00:31:14,280 Speaker 1: Y See you tomorrow, folks. Keep looking on the bright side.