1 00:00:00,040 --> 00:00:03,400 Carol Cox: Your expertise and your voice can shape a 2 00:00:03,400 --> 00:00:04,480 public policy. 3 00:00:04,520 --> 00:00:06,400 You're going to learn frameworks that you can 4 00:00:06,400 --> 00:00:09,320 use to influence policy makers. 5 00:00:09,320 --> 00:00:11,520 With my guest, Doctor Deborah Stine, 6 00:00:11,560 --> 00:00:13,560 on this episode of the Speaking Your Brand 7 00:00:13,560 --> 00:00:19,920 podcast. More and more women are making an 8 00:00:19,920 --> 00:00:22,040 impact by starting businesses, 9 00:00:22,040 --> 00:00:24,760 running for office and speaking up for what 10 00:00:24,760 --> 00:00:27,760 matters. With my background as a TV political 11 00:00:27,760 --> 00:00:29,320 analyst, entrepreneur, 12 00:00:29,320 --> 00:00:32,000 and speaker, I interview and coach purpose 13 00:00:32,000 --> 00:00:34,400 driven women to shape their brands, 14 00:00:34,400 --> 00:00:36,760 grow their companies, and become recognized 15 00:00:36,760 --> 00:00:39,080 as influencers in their field. 16 00:00:39,720 --> 00:00:41,640 This is speaking your brand, 17 00:00:41,640 --> 00:00:44,080 your place to learn how to persuasively 18 00:00:44,080 --> 00:00:47,320 communicate your message to your audience. 19 00:00:48,680 --> 00:00:50,040 Hi there and welcome to the Speaking Your 20 00:00:50,040 --> 00:00:51,440 Brand podcast. I'm your host, 21 00:00:51,440 --> 00:00:54,000 Carol Cox. Today we're diving into a 22 00:00:54,000 --> 00:00:56,240 conversation that I know will resonate with 23 00:00:56,240 --> 00:00:58,320 many of you who listen to the show, 24 00:00:58,560 --> 00:01:01,570 because I know that you're here not to just 25 00:01:01,570 --> 00:01:04,370 have expertise, but you really want to make a 26 00:01:04,370 --> 00:01:05,490 real impact. 27 00:01:05,490 --> 00:01:06,850 And, you know, if you've been listening to me 28 00:01:06,850 --> 00:01:08,930 for a while, that I talk about this idea of 29 00:01:08,930 --> 00:01:11,250 escaping the expert trap, 30 00:01:11,290 --> 00:01:13,450 as I always say, I want you to be experts 31 00:01:13,450 --> 00:01:15,170 with your clients, with the work that you're 32 00:01:15,170 --> 00:01:16,890 doing, because that's why they want you for 33 00:01:16,890 --> 00:01:17,970 your expertise. 34 00:01:18,210 --> 00:01:20,650 But when it comes to using your voice, 35 00:01:20,650 --> 00:01:23,090 to sharing what matters to you with your 36 00:01:23,090 --> 00:01:25,010 audience and what matters to them, 37 00:01:25,530 --> 00:01:28,170 what happens is that we so often stay behind 38 00:01:28,170 --> 00:01:31,130 this kind of curtain of information and data, 39 00:01:31,130 --> 00:01:34,170 instead of sharing our ideas publicly in a 40 00:01:34,170 --> 00:01:35,850 bigger, bolder way. 41 00:01:36,090 --> 00:01:38,610 Well, my guest today has dedicated her career 42 00:01:38,610 --> 00:01:41,730 to helping brilliant people do exactly that. 43 00:01:42,090 --> 00:01:43,610 Deborah Stine has a PhD, 44 00:01:43,610 --> 00:01:45,410 and she's the founder of the Science and 45 00:01:45,410 --> 00:01:47,490 Technology Policy Academy, 46 00:01:47,490 --> 00:01:48,730 where she helps scientists, 47 00:01:48,730 --> 00:01:51,210 engineers and health professionals translate 48 00:01:51,210 --> 00:01:53,810 what they know into policies that improve 49 00:01:53,810 --> 00:01:55,090 people's lives. 50 00:01:55,330 --> 00:01:57,330 And her background is incredible. 51 00:01:57,460 --> 00:02:00,300 It's like a dream to read on LinkedIn. 52 00:02:00,780 --> 00:02:02,140 Deborah has worked with the National 53 00:02:02,140 --> 00:02:03,540 Academies of Sciences, 54 00:02:03,540 --> 00:02:06,140 engineering and Medicine at the Congressional 55 00:02:06,140 --> 00:02:08,500 Research Service, and probably my favorite 56 00:02:08,500 --> 00:02:09,580 position she's had. 57 00:02:09,780 --> 00:02:11,620 She was the executive director of the 58 00:02:11,620 --> 00:02:13,980 President's Council of Advisors on Science 59 00:02:13,980 --> 00:02:16,860 and Technology in the Obama White House. 60 00:02:17,060 --> 00:02:18,900 She was a professor of the practice 61 00:02:18,940 --> 00:02:21,100 engineering and public policy at Carnegie 62 00:02:21,140 --> 00:02:22,460 Mellon University. 63 00:02:22,580 --> 00:02:25,580 So, yes, Debbie has a lot of credentials and 64 00:02:25,580 --> 00:02:27,180 she puts them to good use. 65 00:02:27,220 --> 00:02:29,740 She also knows a thing or two about using 66 00:02:29,740 --> 00:02:32,100 your voice in Rooms of Power. 67 00:02:32,300 --> 00:02:34,260 We're going to talk about the book that she's 68 00:02:34,260 --> 00:02:36,460 written, the work that she does, 69 00:02:36,620 --> 00:02:38,420 and how you don't have to be a political 70 00:02:38,420 --> 00:02:40,500 insider to shape policy. 71 00:02:40,980 --> 00:02:42,740 So we're going to talk through all of these 72 00:02:42,740 --> 00:02:45,340 different ideas, including her framework, 73 00:02:45,340 --> 00:02:47,700 so that you can figure out how to craft your 74 00:02:47,700 --> 00:02:49,740 message to create change. 75 00:02:49,900 --> 00:02:51,500 Debbie, welcome to the podcast. 76 00:02:51,820 --> 00:02:53,580 Deborah Stine: Thanks, Carol. So glad to be here. 77 00:02:54,220 --> 00:02:56,550 Carol Cox: All right. So let's let's talk about the work 78 00:02:56,550 --> 00:02:58,670 that you do now, which is your Science and 79 00:02:58,670 --> 00:03:00,910 Technology Policy Academy. 80 00:03:01,190 --> 00:03:04,510 What does what made you decide to leave kind 81 00:03:04,510 --> 00:03:06,870 of the the DC political world, 82 00:03:06,910 --> 00:03:08,630 you know, working at all of these impressive 83 00:03:08,630 --> 00:03:10,590 places to go out on your own? 84 00:03:11,550 --> 00:03:13,710 Deborah Stine: Well, you know, when I left the the white 85 00:03:13,710 --> 00:03:15,790 House, I was trying to kind of figure out 86 00:03:15,790 --> 00:03:18,310 what I wanted to do. And I got an offer from 87 00:03:18,310 --> 00:03:19,870 Carnegie Mellon University and ended up 88 00:03:19,870 --> 00:03:21,150 moving to where I still am, 89 00:03:21,150 --> 00:03:22,750 which is Pittsburgh, Pennsylvania, 90 00:03:22,750 --> 00:03:25,150 which I never thought I would in my entire 91 00:03:25,150 --> 00:03:28,790 life. Um, and, uh, what I found, 92 00:03:28,870 --> 00:03:31,510 uh, is, is I was working in energy policy, 93 00:03:31,630 --> 00:03:34,110 primarily energy, environmental policy. 94 00:03:34,150 --> 00:03:35,990 There's a lot of things related here to 95 00:03:36,030 --> 00:03:37,790 hydraulic fracturing, shale gas, 96 00:03:37,830 --> 00:03:40,150 things like that. And those are the classes I 97 00:03:40,150 --> 00:03:42,030 teach. And I worked on something called the, 98 00:03:42,070 --> 00:03:44,510 uh, establishing the Scott Institute for 99 00:03:44,510 --> 00:03:45,790 Energy Innovation. 100 00:03:46,470 --> 00:03:48,270 And, you know, after working, 101 00:03:48,310 --> 00:03:51,110 I was in DC for like 30 years, 102 00:03:51,110 --> 00:03:52,350 and that had been my dream. 103 00:03:52,390 --> 00:03:54,750 I started as a field engineer in Texas. 104 00:03:54,990 --> 00:03:56,950 And I made it from there to D.C. 105 00:03:57,430 --> 00:03:58,350 you know, like I said, I worked for the 106 00:03:58,350 --> 00:04:00,030 Congress, worked for the white House. 107 00:04:00,310 --> 00:04:02,750 Um, and, you know, when I came, 108 00:04:02,870 --> 00:04:06,110 um, to university and I started working again 109 00:04:06,110 --> 00:04:07,750 at the state and local level, 110 00:04:07,750 --> 00:04:09,550 which is where I started out in my in my 111 00:04:09,550 --> 00:04:12,230 Texas days, you know, I really found that I 112 00:04:12,230 --> 00:04:14,950 enjoyed being at that ground level again, 113 00:04:14,950 --> 00:04:17,470 because, you know, when you're in D.C., um, 114 00:04:17,510 --> 00:04:18,630 you know, like I worked on state 115 00:04:18,630 --> 00:04:21,750 manufacturing policy, establishing something 116 00:04:21,750 --> 00:04:22,870 called the Advanced Manufacturing 117 00:04:22,870 --> 00:04:25,350 Partnership. Um, there's things all over the 118 00:04:25,350 --> 00:04:27,310 country now which, um, 119 00:04:27,510 --> 00:04:30,270 are things to kind of help advanced 120 00:04:30,270 --> 00:04:31,470 manufacturing. 121 00:04:31,830 --> 00:04:33,790 Uh, and I did a lot of things related to 122 00:04:33,830 --> 00:04:35,190 manufacturing workforce. 123 00:04:35,550 --> 00:04:37,470 Uh, when I came here, all of a sudden, 124 00:04:37,470 --> 00:04:39,110 I could start working, like, okay, 125 00:04:39,150 --> 00:04:41,990 trying to help people who are unemployed get 126 00:04:41,990 --> 00:04:43,390 those manufacturing jobs. 127 00:04:43,430 --> 00:04:45,430 You know, I did focus groups with people who 128 00:04:45,470 --> 00:04:46,670 were unemployed. 129 00:04:46,950 --> 00:04:48,870 Um, I worked with people who are from 130 00:04:49,070 --> 00:04:51,310 underrepresented groups who were trying to, 131 00:04:51,350 --> 00:04:52,840 like, just make a living, 132 00:04:53,200 --> 00:04:55,480 all sorts of things. And the same thing was 133 00:04:55,480 --> 00:04:58,120 true with in energy and environmental issues. 134 00:04:58,120 --> 00:05:00,760 And I really started to enjoy again being 135 00:05:00,800 --> 00:05:02,680 like right at the forefront. 136 00:05:02,720 --> 00:05:04,560 I mean, I think most people think at the 137 00:05:04,560 --> 00:05:06,480 national level you're at the forefront and 138 00:05:06,480 --> 00:05:08,200 you're at the forefront of policy, 139 00:05:08,600 --> 00:05:11,280 but you don't necessarily see those like day 140 00:05:11,280 --> 00:05:14,160 to day impacts at the national level that you 141 00:05:14,160 --> 00:05:16,800 will at the state and local level. 142 00:05:17,840 --> 00:05:19,600 Carol Cox: And so tell me a little bit about what your 143 00:05:19,600 --> 00:05:21,000 academy does now. 144 00:05:21,040 --> 00:05:23,080 What how do who do you help and how do you 145 00:05:23,120 --> 00:05:23,760 help them? 146 00:05:24,400 --> 00:05:25,960 Deborah Stine: Well, when I first, uh, 147 00:05:25,960 --> 00:05:27,880 you know, left Carnegie Mellon University, 148 00:05:28,280 --> 00:05:30,000 I thought that I was going to go and start 149 00:05:30,000 --> 00:05:31,680 working for somebody again. And I and I 150 00:05:31,680 --> 00:05:33,840 interviewed for jobs in DC to be like an 151 00:05:33,840 --> 00:05:35,720 executive director and things like that. And, 152 00:05:35,800 --> 00:05:37,920 you know, it just didn't appeal to me 153 00:05:37,920 --> 00:05:41,040 anymore. Um, I really wanted to basically be 154 00:05:41,080 --> 00:05:43,280 at home with my dogs, looking, 155 00:05:43,320 --> 00:05:45,320 looking, looking at the horizon. 156 00:05:45,360 --> 00:05:47,000 You know, I just wanted to be independent 157 00:05:47,040 --> 00:05:48,920 right after working for other people for so 158 00:05:48,920 --> 00:05:51,850 many years. And the thing in policy is you 159 00:05:51,850 --> 00:05:53,730 really to have an impact, 160 00:05:53,730 --> 00:05:55,170 you really have to be behind the scenes, 161 00:05:55,210 --> 00:05:56,970 right? So I could not really express my own 162 00:05:56,970 --> 00:05:59,050 voice. And what happened was, 163 00:05:59,050 --> 00:06:01,290 is that as I was kind of looking and deciding 164 00:06:01,290 --> 00:06:04,010 what I wanted to do, what became what were 165 00:06:04,010 --> 00:06:06,290 originally side gigs, things just to kind of 166 00:06:06,330 --> 00:06:08,170 earn money. All of a sudden I realized, 167 00:06:08,170 --> 00:06:10,370 oh my goodness, I could make a business out 168 00:06:10,370 --> 00:06:13,410 of this. And I it was only because I was 169 00:06:13,450 --> 00:06:15,010 actually talking to somebody who was actually 170 00:06:15,010 --> 00:06:16,130 a staff member at one of the major 171 00:06:16,130 --> 00:06:17,650 foundations here in Pittsburgh, and he said, 172 00:06:17,690 --> 00:06:19,770 you know, Debbie, you should form an LLC. 173 00:06:19,770 --> 00:06:21,810 And I thought, oh, I guess I could. 174 00:06:22,130 --> 00:06:24,130 And then, um, you know, 175 00:06:24,370 --> 00:06:26,410 as soon as I sort of let people know that I 176 00:06:26,410 --> 00:06:28,810 was leaving, I started getting like offers of 177 00:06:28,810 --> 00:06:31,090 people to, like, do work, 178 00:06:31,410 --> 00:06:33,250 and I and I thought, oh, 179 00:06:33,290 --> 00:06:34,650 you know, maybe I can do this. 180 00:06:34,650 --> 00:06:36,090 And then it took me a long time, 181 00:06:36,090 --> 00:06:38,090 maybe six months or so at least, 182 00:06:38,410 --> 00:06:39,610 to say, hey, you know, 183 00:06:39,650 --> 00:06:41,850 I can make a business. And then over time, 184 00:06:42,250 --> 00:06:45,290 I definitely, like, earn as much as I did 185 00:06:45,290 --> 00:06:46,570 when I was a professor. 186 00:06:46,810 --> 00:06:49,300 Um, and I have a lot more independence. 187 00:06:49,460 --> 00:06:50,940 Um, you know, as you know, 188 00:06:50,940 --> 00:06:51,980 I just came back from, 189 00:06:52,020 --> 00:06:53,780 like, a month in southern Africa. 190 00:06:54,180 --> 00:06:55,860 Um, back in March. 191 00:06:55,860 --> 00:06:58,820 I spent another month around in Antarctica. 192 00:06:59,180 --> 00:07:01,540 Uh, those are things I never could have done 193 00:07:01,540 --> 00:07:04,220 when I was a professor or any of those jobs 194 00:07:04,220 --> 00:07:05,900 that I had in DC. 195 00:07:05,940 --> 00:07:08,700 And so in terms of quality of life, 196 00:07:08,940 --> 00:07:10,860 uh, it's definitely much, 197 00:07:10,860 --> 00:07:12,460 much better than I had. 198 00:07:12,460 --> 00:07:13,940 So, you know, when I left, 199 00:07:13,940 --> 00:07:16,180 I started with like sort of two, 200 00:07:17,140 --> 00:07:18,900 two firms. One was consulting, 201 00:07:19,180 --> 00:07:21,300 which was analysis. And that's still where I 202 00:07:21,420 --> 00:07:23,260 get most of my income is analysis. 203 00:07:23,540 --> 00:07:26,740 Um, uh, and then the second part is more 204 00:07:26,780 --> 00:07:29,620 things that are teaching workshops, 205 00:07:29,620 --> 00:07:31,860 coaching, um, you know, 206 00:07:31,900 --> 00:07:34,900 giving keynotes, those kinds of things. 207 00:07:35,140 --> 00:07:37,140 And I actually had them as two separate 208 00:07:37,140 --> 00:07:39,660 companies. And then over time, 209 00:07:39,700 --> 00:07:41,260 again, this maybe took a couple of years. I 210 00:07:41,260 --> 00:07:42,620 realized, you know, why am I doing this? I 211 00:07:42,620 --> 00:07:44,420 need one brand, right? 212 00:07:44,700 --> 00:07:48,710 And I decided of the two companies that I had 213 00:07:49,070 --> 00:07:52,110 that the one that had the most visibility 214 00:07:52,110 --> 00:07:54,470 that people understood the most was a science 215 00:07:54,470 --> 00:07:55,950 technology policy academy. 216 00:07:56,190 --> 00:07:59,070 A lot of my analysis is confidential. 217 00:07:59,350 --> 00:08:01,110 It's kind of behind the scenes. 218 00:08:01,350 --> 00:08:04,710 Um, and so that brand never really had a lot 219 00:08:04,710 --> 00:08:06,910 of visibility, but the Science Technology 220 00:08:06,910 --> 00:08:08,910 Policy Academy, because it was things that 221 00:08:08,910 --> 00:08:10,790 were much more public, like workshops, 222 00:08:10,790 --> 00:08:13,710 coaching and and things of that nature. 223 00:08:13,990 --> 00:08:15,590 Uh, it did have a lot of visibility. 224 00:08:15,590 --> 00:08:18,390 And so I as opposed to the, 225 00:08:18,430 --> 00:08:20,430 I should say, when I started, I did my own 226 00:08:20,430 --> 00:08:22,030 website and everything else. 227 00:08:22,270 --> 00:08:24,150 I actually hired a professional, 228 00:08:24,350 --> 00:08:26,430 did something that was a quality, 229 00:08:26,590 --> 00:08:29,470 uh, website that included all the different 230 00:08:29,470 --> 00:08:30,710 lines of business that I, 231 00:08:30,870 --> 00:08:33,430 um, that I have and engage in. 232 00:08:33,710 --> 00:08:35,550 Um, and, uh, like I said, 233 00:08:35,550 --> 00:08:37,430 I've been able to make it successful. 234 00:08:37,430 --> 00:08:39,470 Now, I think I'm getting on close to like ten 235 00:08:39,470 --> 00:08:41,310 years or so since I, since I left, 236 00:08:41,310 --> 00:08:43,350 uh, um, Carnegie Mellon. 237 00:08:44,310 --> 00:08:46,880 Carol Cox: Well, let's talk a little bit about this idea 238 00:08:46,880 --> 00:08:48,880 of policy, public policy, 239 00:08:48,880 --> 00:08:51,240 because I'm sure that for many listeners, 240 00:08:51,240 --> 00:08:53,400 especially those who've never been involved 241 00:08:53,400 --> 00:08:55,840 in politics or even thought about influencing 242 00:08:55,840 --> 00:08:57,880 policy, or especially if they don't live in 243 00:08:57,880 --> 00:08:59,160 the Washington, D.C. area, 244 00:08:59,160 --> 00:09:00,920 they don't really come across it. 245 00:09:00,920 --> 00:09:04,120 So in in their lives, they may think, 246 00:09:04,120 --> 00:09:06,640 well, how can I possibly impact policy? 247 00:09:06,680 --> 00:09:08,200 Like, I'm just one person. 248 00:09:08,560 --> 00:09:10,960 And even though I may have certain research 249 00:09:10,960 --> 00:09:13,040 that I've done, maybe I am a scientist or I'm 250 00:09:13,040 --> 00:09:15,880 an engineer or I'm a health professional. 251 00:09:15,880 --> 00:09:17,400 So I do have expertise, 252 00:09:17,400 --> 00:09:19,320 and I do have important things that could 253 00:09:19,320 --> 00:09:22,240 impact policy, but I don't even know where to 254 00:09:22,240 --> 00:09:23,640 start or what to do. 255 00:09:23,680 --> 00:09:24,720 What would you tell them? 256 00:09:25,560 --> 00:09:26,680 Deborah Stine: Well, the first thing is, 257 00:09:26,680 --> 00:09:29,800 is that oftentimes people sort of conflate 258 00:09:29,800 --> 00:09:31,960 policy and politics, right? 259 00:09:32,200 --> 00:09:33,600 I don't do politics. 260 00:09:33,760 --> 00:09:35,360 I mean, you know, I did work for the white 261 00:09:35,360 --> 00:09:37,240 House for for the first three years of the 262 00:09:37,240 --> 00:09:38,920 Obama administration, but I was not involved 263 00:09:38,920 --> 00:09:40,160 in the Obama campaign. 264 00:09:40,480 --> 00:09:42,600 You know, I was they basically just called me 265 00:09:42,640 --> 00:09:45,640 one day, asked interview me and voila. 266 00:09:45,800 --> 00:09:48,320 I you know, that's how I sort of got the 267 00:09:48,560 --> 00:09:53,600 position. Um, and policy is really very, 268 00:09:53,640 --> 00:09:54,840 uh, analytical. Right. 269 00:09:54,880 --> 00:09:56,520 So it's things where like, 270 00:09:56,520 --> 00:09:58,840 what I teach is neutral policy analysis, 271 00:09:58,840 --> 00:10:01,320 where, uh, speaking truth to power, 272 00:10:01,320 --> 00:10:03,040 that means that I'm doing the analysis. 273 00:10:03,040 --> 00:10:06,480 I'm going to tell the policymaker that maybe 274 00:10:06,520 --> 00:10:08,680 the their favorite idea that they really 275 00:10:08,680 --> 00:10:11,160 loved may not be a great idea. 276 00:10:11,160 --> 00:10:12,560 And I'm going to tell them why. 277 00:10:13,160 --> 00:10:16,240 Uh, and that's, I think, 278 00:10:16,280 --> 00:10:17,600 super important. Right. 279 00:10:17,640 --> 00:10:19,800 To understand that, you need to take an 280 00:10:19,840 --> 00:10:23,040 analytical approach to policy. 281 00:10:23,480 --> 00:10:24,960 It's not just about like, 282 00:10:25,000 --> 00:10:26,600 oh, this is my opinion. 283 00:10:26,880 --> 00:10:30,400 But really, when you're giving your opinion 284 00:10:30,400 --> 00:10:32,200 based on analysis, uh, 285 00:10:32,800 --> 00:10:34,200 it will have substance. 286 00:10:34,520 --> 00:10:36,880 So I'm going to just give an example just 287 00:10:36,880 --> 00:10:38,000 because I happen to see it, 288 00:10:38,080 --> 00:10:41,000 uh, in the newspaper this morning, 289 00:10:41,080 --> 00:10:44,370 uh, Actually the Pittsburgh newspaper. 290 00:10:44,370 --> 00:10:48,530 So one decision that the Trump administration 291 00:10:48,530 --> 00:10:53,450 has made is to say that that nursing is no 292 00:10:53,450 --> 00:10:56,010 longer a professional degree. 293 00:10:56,370 --> 00:10:58,890 Okay. Now this has first, 294 00:10:58,930 --> 00:11:00,650 it's got to be one of the silliest decisions 295 00:11:00,650 --> 00:11:01,810 like, ever, right. 296 00:11:02,050 --> 00:11:06,170 Um, and but it has major implications for 297 00:11:06,170 --> 00:11:08,530 something where we, we a we know there's a 298 00:11:08,570 --> 00:11:10,370 shortage of nurses, right? 299 00:11:10,410 --> 00:11:11,890 Nurses are like, super, 300 00:11:11,890 --> 00:11:14,850 super important as anybody who's ever had to, 301 00:11:15,210 --> 00:11:16,810 you know, have health care or go to the 302 00:11:16,810 --> 00:11:18,490 hospital or have, you know, 303 00:11:18,530 --> 00:11:19,770 a relative who's been sick, 304 00:11:19,770 --> 00:11:21,530 know that nurses are often even more 305 00:11:21,530 --> 00:11:22,970 important than doctors. I think, 306 00:11:22,970 --> 00:11:24,650 in terms of your day to day care. 307 00:11:25,130 --> 00:11:31,370 And, um, but if this decision is kept right, 308 00:11:31,650 --> 00:11:35,250 then it means that it'll be harder for people 309 00:11:35,250 --> 00:11:37,210 who do seek nursing degrees to, 310 00:11:37,250 --> 00:11:38,850 like, get their loans repaid. 311 00:11:38,890 --> 00:11:40,540 And, you know, there's all sorts of negative 312 00:11:40,540 --> 00:11:43,420 implications. So the person who wrote this 313 00:11:43,420 --> 00:11:46,340 article and is a very long piece, 314 00:11:46,620 --> 00:11:48,380 uh, an opinion article, 315 00:11:48,460 --> 00:11:50,660 uh, explained this, right. 316 00:11:50,820 --> 00:11:53,260 And people understand it. 317 00:11:53,300 --> 00:11:55,660 It's something that when you really, 318 00:11:56,500 --> 00:11:57,980 you know, anybody like I said, 319 00:11:57,980 --> 00:11:59,700 everybody's had somebody who's been ill in 320 00:11:59,700 --> 00:12:02,340 their family. And he said, 321 00:12:02,340 --> 00:12:03,540 okay, well that's not fair. 322 00:12:03,660 --> 00:12:06,260 Right. And that's what you really are going 323 00:12:06,260 --> 00:12:09,860 for when you are talking about an issue. 324 00:12:10,580 --> 00:12:13,500 Your goal is for the other side. 325 00:12:13,540 --> 00:12:14,900 You know, people on the other side, people 326 00:12:14,900 --> 00:12:17,020 generally who are unaware this is even an 327 00:12:17,020 --> 00:12:19,300 issue will understand, 328 00:12:19,740 --> 00:12:22,300 hey, this is something that I should care 329 00:12:22,300 --> 00:12:24,460 about. This is something that is not fair, 330 00:12:24,860 --> 00:12:28,140 right? Another thing that just happened this 331 00:12:28,140 --> 00:12:31,460 week is Doge is finally at an end, 332 00:12:31,500 --> 00:12:33,460 right? Well, why did that happen? 333 00:12:33,500 --> 00:12:34,660 Like it took months, right? 334 00:12:34,740 --> 00:12:36,220 I mean, we're now in December. 335 00:12:36,260 --> 00:12:38,350 You know, this chaos started in January, 336 00:12:38,350 --> 00:12:39,870 so it took like a whole year. 337 00:12:40,270 --> 00:12:42,990 But in the end, I think it's because people 338 00:12:43,030 --> 00:12:46,350 saw that the decisions that were being made 339 00:12:46,550 --> 00:12:47,630 were not fair. 340 00:12:47,870 --> 00:12:49,390 Um, I had, for example, 341 00:12:49,430 --> 00:12:51,070 like somebody that I had been working with 342 00:12:51,110 --> 00:12:53,110 for a while at West Virginia University, 343 00:12:53,390 --> 00:12:55,710 uh, she had a PhD, she had postdoc. 344 00:12:55,710 --> 00:12:57,870 She got her dream job with the Department of 345 00:12:57,870 --> 00:13:01,270 Interior. She moved her family with two small 346 00:13:01,270 --> 00:13:03,310 kids. Her husband quit her job. 347 00:13:03,310 --> 00:13:04,590 They moved to California, 348 00:13:04,630 --> 00:13:05,790 southern California. 349 00:13:06,230 --> 00:13:09,070 She was in the job for two weeks before she 350 00:13:09,070 --> 00:13:12,110 lost her job. Okay, because she was a 351 00:13:12,110 --> 00:13:13,670 provisional employee, she didn't even have a 352 00:13:13,670 --> 00:13:14,710 chance to get started. 353 00:13:14,710 --> 00:13:17,030 Why? Because what she was doing for the 354 00:13:17,030 --> 00:13:20,030 Department of Interior had the word equity in 355 00:13:20,030 --> 00:13:21,830 it. Right? And that was it. 356 00:13:21,870 --> 00:13:23,790 It wasn't like there was any judgment about 357 00:13:23,790 --> 00:13:25,270 the job or anything else. 358 00:13:25,310 --> 00:13:27,590 It was because it had one word. 359 00:13:27,630 --> 00:13:31,630 Right. And I do have a happy end to the 360 00:13:31,630 --> 00:13:34,070 story. She was eventually able to get a job, 361 00:13:34,550 --> 00:13:36,150 um, you know, with. 362 00:13:36,230 --> 00:13:38,720 Well, first. First, there was a lawsuit that 363 00:13:38,720 --> 00:13:41,040 that led her to actually getting paid. 364 00:13:41,040 --> 00:13:42,240 So you can imagine, again, 365 00:13:42,480 --> 00:13:44,040 you had to buy a new house. Again, 366 00:13:44,040 --> 00:13:45,920 this is again where people say it's not fair. 367 00:13:45,960 --> 00:13:48,320 You move your family, you change everything. 368 00:13:48,360 --> 00:13:50,120 You bought a house and all of a sudden, 369 00:13:50,520 --> 00:13:52,560 okay, chaos happens, right? 370 00:13:53,080 --> 00:13:56,320 And, um, like I said, eventually a lawsuit 371 00:13:56,320 --> 00:13:59,160 went and gave her some back pay that kept her 372 00:13:59,160 --> 00:14:00,680 going for like six months or so. 373 00:14:00,720 --> 00:14:02,520 She now has a new job with the state of 374 00:14:02,520 --> 00:14:04,680 California. But still, 375 00:14:04,720 --> 00:14:07,080 people understand this stuff is not fair. 376 00:14:07,120 --> 00:14:08,480 And sometimes it takes time. 377 00:14:08,520 --> 00:14:09,800 And that's one thing you have to understand 378 00:14:09,800 --> 00:14:11,840 with policy is that, you know, 379 00:14:12,160 --> 00:14:13,920 you know, it can take a year. It can take a 380 00:14:13,920 --> 00:14:16,600 decade. I mean, things can take a long time. 381 00:14:16,880 --> 00:14:19,640 But eventually, if you keep on speaking truth 382 00:14:19,640 --> 00:14:22,720 to power, eventually things will get 383 00:14:22,720 --> 00:14:25,240 corrected. And that is sort of the good thing 384 00:14:25,240 --> 00:14:27,160 about our democracy. 385 00:14:27,640 --> 00:14:29,760 Um, you know, one thing I always say is that 386 00:14:29,760 --> 00:14:30,800 if you work in policy, 387 00:14:30,800 --> 00:14:32,240 you have to be an optimist. 388 00:14:32,280 --> 00:14:37,210 You have to believe That what you do and 389 00:14:37,210 --> 00:14:40,650 things you do will eventually lead to change. 390 00:14:40,650 --> 00:14:43,090 And I've been lucky throughout my career. 391 00:14:43,090 --> 00:14:46,050 I can tell you probably 20 or 30 things where 392 00:14:46,050 --> 00:14:49,530 we have a better world because of studies and 393 00:14:49,530 --> 00:14:52,050 analysis that I have worked on with teams of 394 00:14:52,050 --> 00:14:54,850 experts on all sorts of issues in health, 395 00:14:54,850 --> 00:14:57,090 energy, environment, health, 396 00:14:57,130 --> 00:14:58,690 IT, and like you name it. 397 00:14:59,410 --> 00:15:00,530 Carol Cox: Yeah. And Debbie. Well, 398 00:15:00,530 --> 00:15:01,930 thank you for sharing those examples. It 399 00:15:01,930 --> 00:15:03,890 brought to mind a couple that that I hadn't 400 00:15:03,890 --> 00:15:05,770 thought of until this conversation here. 401 00:15:05,810 --> 00:15:07,930 So one of the women I've worked with in the 402 00:15:07,930 --> 00:15:10,330 past, she's a pediatrician and she runs a 403 00:15:10,330 --> 00:15:12,610 group called Women in Pediatrics for other 404 00:15:12,610 --> 00:15:14,090 women pediatricians. 405 00:15:14,090 --> 00:15:16,490 And I know that there are several of them 406 00:15:16,490 --> 00:15:18,810 that I have talked to who oftentimes will go 407 00:15:18,810 --> 00:15:20,890 to their state capitals and the states where 408 00:15:20,890 --> 00:15:23,490 they live and advocate on behalf of their 409 00:15:23,490 --> 00:15:25,730 patients. So the kids, 410 00:15:25,730 --> 00:15:27,290 for whatever it is that they need, that the 411 00:15:27,290 --> 00:15:29,570 legislature is considering some bill which 412 00:15:29,570 --> 00:15:32,570 may be detrimental to children's health in 413 00:15:32,570 --> 00:15:34,450 one way or another, so they will very much 414 00:15:34,490 --> 00:15:36,890 use their voice and go and do that, 415 00:15:36,890 --> 00:15:40,850 or write op eds in the local papers or in 416 00:15:40,850 --> 00:15:42,290 some type of publication, 417 00:15:42,450 --> 00:15:44,090 again, taking their expertise, 418 00:15:44,090 --> 00:15:46,650 but then putting a story and, 419 00:15:46,690 --> 00:15:48,930 and their face and their voice on it, 420 00:15:48,930 --> 00:15:51,330 instead of just having it be some abstract 421 00:15:51,330 --> 00:15:53,370 bill that the legislature is considering. 422 00:15:53,610 --> 00:15:55,650 Deborah Stine: Right. And so that's a really good example. 423 00:15:55,690 --> 00:15:57,970 Like, one of the things that I really tried 424 00:15:57,970 --> 00:16:00,290 to emphasize with my clients is that, 425 00:16:00,610 --> 00:16:03,290 you know, we think about policies being a 426 00:16:03,290 --> 00:16:05,370 federal thing, working in government, 427 00:16:05,370 --> 00:16:08,290 but there's lots of very important policies, 428 00:16:08,290 --> 00:16:09,690 particularly in health care. 429 00:16:09,930 --> 00:16:12,130 Um, you know, but also all these other issues 430 00:16:12,130 --> 00:16:14,250 as well there at the state and local level. 431 00:16:14,570 --> 00:16:18,490 And you can have just as much impact by 432 00:16:18,530 --> 00:16:19,850 telling your stories. And, 433 00:16:20,130 --> 00:16:22,130 you know, when I teach people how to 434 00:16:22,210 --> 00:16:25,570 communicate, one thing I always emphasize is 435 00:16:25,610 --> 00:16:27,630 that I teach something called like a one 436 00:16:27,630 --> 00:16:29,170 minute policy pitch. 437 00:16:29,210 --> 00:16:31,980 Right. And and there's a couple of 438 00:16:31,980 --> 00:16:33,540 principles. One of the one of the first 439 00:16:33,540 --> 00:16:36,700 principles is what this bottom line up front, 440 00:16:36,860 --> 00:16:38,140 at the very beginning, 441 00:16:38,140 --> 00:16:40,100 you say this to the policymaker, 442 00:16:40,100 --> 00:16:43,420 this is what you want to do in the middle. 443 00:16:43,540 --> 00:16:46,900 What you do is you always tell a story with a 444 00:16:46,900 --> 00:16:48,660 real person, right? 445 00:16:48,700 --> 00:16:50,900 So I would bet those pediatricians, 446 00:16:51,100 --> 00:16:52,780 you know, can say, you know, 447 00:16:52,820 --> 00:16:54,980 like, okay, this happened to this person, 448 00:16:54,980 --> 00:16:56,660 this happened to that person. 449 00:16:57,020 --> 00:16:59,580 And that's what really makes an impact on 450 00:16:59,580 --> 00:17:01,540 policy makers. If you look at, 451 00:17:01,580 --> 00:17:03,980 just for example, the state of the Union 452 00:17:03,980 --> 00:17:05,340 speech, which is every year, 453 00:17:06,100 --> 00:17:09,260 and there's always people that are kind of up 454 00:17:09,260 --> 00:17:10,740 in the galley, right. 455 00:17:10,780 --> 00:17:12,540 And what they do is so they can point to that 456 00:17:12,540 --> 00:17:14,540 person and say so and so this happened to 457 00:17:14,580 --> 00:17:15,740 that person or so-and-so, 458 00:17:15,780 --> 00:17:17,060 this happened to that person. 459 00:17:17,340 --> 00:17:19,540 And out of like, it could be a one hour 460 00:17:19,540 --> 00:17:21,900 speech, 45 minutes, one hour for the state of 461 00:17:21,900 --> 00:17:24,980 the Union. The only thing people remember is 462 00:17:25,020 --> 00:17:26,260 those stories, right? 463 00:17:26,260 --> 00:17:29,750 Right, right. And when I have people do In 464 00:17:29,830 --> 00:17:32,070 workshops where I teach people how to do 465 00:17:32,110 --> 00:17:33,230 policy pitches. 466 00:17:33,230 --> 00:17:35,110 And sometimes there's competitions, 467 00:17:35,150 --> 00:17:36,230 you know, like with them, 468 00:17:36,230 --> 00:17:38,430 like so I travel around the country and do 469 00:17:38,430 --> 00:17:41,310 this sort of thing, even though I tell 470 00:17:41,350 --> 00:17:43,430 people, you got to have a story. 471 00:17:43,950 --> 00:17:46,950 Sometimes the people I'm teaching don't put 472 00:17:46,950 --> 00:17:48,870 it in and I have them vote, 473 00:17:48,870 --> 00:17:51,430 right, or I have I have experts vote and 474 00:17:51,430 --> 00:17:54,390 without a doubt, the one that always wins has 475 00:17:54,390 --> 00:17:56,990 a really good story, and the one that always 476 00:17:56,990 --> 00:17:59,470 loses is the ones who ignored me. 477 00:17:59,470 --> 00:18:01,110 And thought facts and figures are going to 478 00:18:01,110 --> 00:18:03,950 make a difference. And it's just, 479 00:18:04,270 --> 00:18:06,390 you know, we all remember stories, 480 00:18:06,430 --> 00:18:08,990 right? That's what really has an impact. 481 00:18:08,990 --> 00:18:11,590 And that's why it's important to, 482 00:18:11,630 --> 00:18:13,350 as you said, op eds are really good. 483 00:18:13,350 --> 00:18:14,830 Like the one I just talked about where this 484 00:18:14,830 --> 00:18:16,630 nurses is, uh, you know, 485 00:18:16,670 --> 00:18:18,110 talking about, you know, you do need those 486 00:18:18,150 --> 00:18:19,670 kind of facts and figures, 487 00:18:19,750 --> 00:18:23,310 um, to, uh, to justify these things. 488 00:18:23,470 --> 00:18:25,830 But what people will remember are the 489 00:18:25,830 --> 00:18:26,510 stories. 490 00:18:27,110 --> 00:18:30,160 Carol Cox: Where do you think the reluctance comes from, 491 00:18:30,160 --> 00:18:31,560 especially on that part, 492 00:18:31,560 --> 00:18:35,080 I feel, of those highly credentialed experts, 493 00:18:35,360 --> 00:18:38,320 their reluctance to include personal stories. 494 00:18:38,320 --> 00:18:40,720 Is it that they haven't seen it modeled for 495 00:18:40,760 --> 00:18:43,600 them in other people and other peers, 496 00:18:43,600 --> 00:18:45,720 so they feel like they it's not something 497 00:18:45,720 --> 00:18:46,960 that they should do as well? 498 00:18:46,960 --> 00:18:48,840 Is it that they don't think of personal 499 00:18:48,840 --> 00:18:51,400 stories, or do they feel like persuasion 500 00:18:51,400 --> 00:18:53,160 comes from the facts and the data? 501 00:18:54,000 --> 00:18:55,840 Deborah Stine: Well, I think it's kind of like all of the 502 00:18:55,840 --> 00:18:57,960 above. Yeah. So so the first one, 503 00:18:57,960 --> 00:19:00,000 when I, when I teach, uh, 504 00:19:00,000 --> 00:19:01,960 how to do policy analysis, 505 00:19:02,000 --> 00:19:03,600 I focus on these four E's. 506 00:19:04,240 --> 00:19:05,880 So the first one is effectiveness. 507 00:19:05,920 --> 00:19:07,800 Effectiveness is the degree to which the 508 00:19:07,800 --> 00:19:09,120 societal goal will be met. 509 00:19:09,640 --> 00:19:11,720 And when we're trained as scientists or 510 00:19:11,720 --> 00:19:13,280 engineers or health professionals, 511 00:19:13,280 --> 00:19:14,560 that's what we always look at. 512 00:19:14,600 --> 00:19:16,120 We only look at that one thing, 513 00:19:16,320 --> 00:19:19,080 you know, will this cure this disease or, 514 00:19:19,280 --> 00:19:21,160 you know, will this solve this environmental 515 00:19:21,160 --> 00:19:25,400 problem or will this innovation make money? 516 00:19:25,440 --> 00:19:27,730 Right? So those. 517 00:19:28,050 --> 00:19:30,450 But that's not how people make decisions, 518 00:19:30,490 --> 00:19:32,770 right? So I don't care if you're buying a 519 00:19:32,770 --> 00:19:34,850 car, buying a house, deciding where to go to 520 00:19:34,850 --> 00:19:36,970 college. You're going to talk about more 521 00:19:36,970 --> 00:19:38,690 about effectiveness. Like there's a million 522 00:19:38,690 --> 00:19:40,530 places you can get a college degree, 523 00:19:40,570 --> 00:19:43,410 right? For example, a car island is, 524 00:19:43,450 --> 00:19:45,170 you know, like you could have any car that 525 00:19:45,170 --> 00:19:47,130 will get you from point A to point B, 526 00:19:47,570 --> 00:19:49,370 but you consider a lot more things, 527 00:19:49,410 --> 00:19:51,650 right. And so that's where the other three 528 00:19:51,650 --> 00:19:54,250 E's come in. So let's just take like you're 529 00:19:54,250 --> 00:19:57,730 buying a car. Um, so that the second E is 530 00:19:57,770 --> 00:20:00,170 efficiency and efficiency is economic 531 00:20:00,170 --> 00:20:02,090 efficiency. And that is that is basically 532 00:20:02,090 --> 00:20:04,250 what will get the most bang for the societal 533 00:20:04,290 --> 00:20:06,050 buck, right? So, you know, 534 00:20:06,090 --> 00:20:08,050 maybe, you know, like when I was young, 535 00:20:08,210 --> 00:20:10,650 um, in fact, in fact, my sister came across a 536 00:20:10,650 --> 00:20:13,210 picture of me, I bought this very cool red 537 00:20:13,250 --> 00:20:15,690 Honda Prelude, a little sports car kind of 538 00:20:15,730 --> 00:20:17,610 thing. And I was, you know, 539 00:20:17,650 --> 00:20:20,090 it was just me. I would tool around my town 540 00:20:20,090 --> 00:20:21,130 back in Corpus Christi, 541 00:20:21,170 --> 00:20:22,930 Texas. That was where I was a field engineer. 542 00:20:23,250 --> 00:20:24,340 And that was great. Okay. 543 00:20:24,380 --> 00:20:25,980 But as I got older, you know, 544 00:20:26,020 --> 00:20:28,300 I had a kid, and then I had to have transport 545 00:20:28,300 --> 00:20:30,580 the grandparents. And then I had the friends 546 00:20:30,580 --> 00:20:32,740 of the kid, and all of a sudden. 547 00:20:32,900 --> 00:20:36,980 Right. You know, I needed more than that. 548 00:20:37,020 --> 00:20:38,660 Right. I needed more than getting from point 549 00:20:38,700 --> 00:20:40,980 A to point B and squeezing everybody in. 550 00:20:41,220 --> 00:20:42,940 But I could only afford so much, 551 00:20:42,940 --> 00:20:44,500 right? Because if you have a kid, 552 00:20:44,540 --> 00:20:47,020 have a house. So I needed to figure out, 553 00:20:47,020 --> 00:20:50,740 well, how can I meet the needs of my family 554 00:20:51,220 --> 00:20:53,300 and at a decent price, 555 00:20:53,340 --> 00:20:55,660 right? And so I changed. 556 00:20:55,660 --> 00:20:57,060 All of a sudden I went to, 557 00:20:57,340 --> 00:20:59,220 like, you know what I have now? 558 00:20:59,260 --> 00:21:01,140 A Honda CRV, right? 559 00:21:01,300 --> 00:21:03,020 Much more practical. Still Honda. 560 00:21:03,060 --> 00:21:05,500 Right? But a different, different thing. The 561 00:21:05,500 --> 00:21:07,300 second thing, the third thing is sort of 562 00:21:07,340 --> 00:21:09,460 equity. Equity is thinking about who are the 563 00:21:09,460 --> 00:21:10,820 winners and who are the losers. 564 00:21:10,820 --> 00:21:13,020 So then you might want to think about, 565 00:21:13,060 --> 00:21:14,820 say like the size of the car, 566 00:21:14,980 --> 00:21:17,420 right. So yeah, sure. 567 00:21:17,860 --> 00:21:19,700 I probably could have had I don't know, 568 00:21:19,740 --> 00:21:21,300 let's just say a Honda Civic, 569 00:21:21,340 --> 00:21:22,420 you know, like, you know, 570 00:21:22,510 --> 00:21:24,590 I could have fit everybody in, 571 00:21:24,950 --> 00:21:27,070 but it would have been kind of a tight 572 00:21:27,110 --> 00:21:28,630 squeeze. Right. 573 00:21:29,070 --> 00:21:33,070 So, you know, that meant that the losers 574 00:21:33,070 --> 00:21:34,350 would be who's ever in the back seat. It 575 00:21:34,350 --> 00:21:35,670 wouldn't be me. Right. 576 00:21:35,710 --> 00:21:37,630 Because, you know, I'm in the driver's seat, 577 00:21:37,910 --> 00:21:40,270 but it would be everybody else sort of in the 578 00:21:40,270 --> 00:21:42,310 back seat. Right. So there's always winners 579 00:21:42,310 --> 00:21:43,870 and losers for every policy. 580 00:21:43,870 --> 00:21:45,870 And you need to understand who those losers 581 00:21:45,870 --> 00:21:47,910 are because you don't want the losers to lose 582 00:21:47,910 --> 00:21:49,830 too much. Right. 583 00:21:49,870 --> 00:21:52,310 But you still have to keep the in these the 584 00:21:52,310 --> 00:21:54,310 effectiveness and the efficiency. 585 00:21:54,310 --> 00:21:55,470 The economic efficiency. 586 00:21:55,910 --> 00:21:57,670 The last one is ease of political 587 00:21:57,670 --> 00:21:59,910 acceptability. So let's just say, 588 00:22:00,430 --> 00:22:03,350 um, that it it doesn't mean like politics, 589 00:22:03,350 --> 00:22:05,790 like politicians. It means who's likely to 590 00:22:05,830 --> 00:22:08,430 oppose or support a policy. 591 00:22:08,470 --> 00:22:10,830 Right. So let's just say I went and thought, 592 00:22:10,830 --> 00:22:11,990 okay, I'm going to get this car, but, you 593 00:22:11,990 --> 00:22:14,030 know, I'm going to get an orange car. 594 00:22:15,110 --> 00:22:18,510 Okay. Now, somehow I think that, 595 00:22:18,550 --> 00:22:20,350 you know, the kids and the grandparents would 596 00:22:20,350 --> 00:22:21,430 say, really, Debbie? 597 00:22:21,470 --> 00:22:22,790 An orange car? 598 00:22:22,910 --> 00:22:24,910 Is that really what we want to be wandering 599 00:22:24,910 --> 00:22:26,750 around the neighborhood in? You know, 600 00:22:26,870 --> 00:22:29,350 or maybe a purple car or all these different 601 00:22:29,350 --> 00:22:33,150 things. People are going to object to certain 602 00:22:33,190 --> 00:22:34,790 things, or maybe people are going to say, 603 00:22:34,790 --> 00:22:36,670 well, you know, like, why are you getting. 604 00:22:36,990 --> 00:22:38,430 Let's just say I said, okay, I'm not going to 605 00:22:38,430 --> 00:22:39,750 get a Honda CRV. 606 00:22:39,990 --> 00:22:41,710 I'm going to get a, you know, 607 00:22:41,750 --> 00:22:43,350 one of these big giant, 608 00:22:43,430 --> 00:22:45,590 uh, SUVs, right? 609 00:22:45,750 --> 00:22:47,230 And so I was like, oh, like Debbie, 610 00:22:47,470 --> 00:22:48,990 you know, like my colleagues are going to 611 00:22:48,990 --> 00:22:52,070 say, hey, you're why are you driving around 612 00:22:52,070 --> 00:22:54,310 the giant SUV? Do you really need that big of 613 00:22:54,310 --> 00:22:56,230 a car? Think about what you're doing to the 614 00:22:56,230 --> 00:22:57,710 environment. Right. 615 00:22:58,350 --> 00:22:59,990 And that goes into equity issues, 616 00:23:00,030 --> 00:23:01,630 ease of accessibility issues. 617 00:23:01,950 --> 00:23:03,430 So those things, you know, 618 00:23:03,950 --> 00:23:06,390 efficiency, effectiveness, 619 00:23:06,390 --> 00:23:10,030 efficiency, equity, ease of accessibility are 620 00:23:10,030 --> 00:23:13,310 really how we make all our decisions that we 621 00:23:13,310 --> 00:23:14,870 just don't really realize it. We kind of 622 00:23:14,870 --> 00:23:17,550 internalize it. Policy makers are the same, 623 00:23:17,710 --> 00:23:19,560 same way. So going back to your original 624 00:23:19,560 --> 00:23:22,560 question, the challenges is that when we are 625 00:23:22,560 --> 00:23:24,160 trained as scientists, 626 00:23:24,160 --> 00:23:25,880 engineers and health professionals, 627 00:23:25,880 --> 00:23:28,120 we are not trained to look at these other 628 00:23:28,120 --> 00:23:30,160 things. We're not trained to look at cost. 629 00:23:30,200 --> 00:23:32,680 We're not trained to look at public 630 00:23:32,680 --> 00:23:33,880 acceptability. 631 00:23:34,240 --> 00:23:36,760 You know, we're not trained to look at equity 632 00:23:36,760 --> 00:23:39,600 issues. Um, and because of this, 633 00:23:39,640 --> 00:23:42,200 we don't do a very good job communicating 634 00:23:42,320 --> 00:23:43,480 because fundamentally, 635 00:23:43,480 --> 00:23:46,200 we have been trained that the facts should 636 00:23:46,200 --> 00:23:47,400 speak for themselves. 637 00:23:47,760 --> 00:23:49,880 And the answer is facts do not speak for 638 00:23:49,880 --> 00:23:50,680 themselves. 639 00:23:51,360 --> 00:23:52,640 Carol Cox: Right. Okay. 640 00:23:52,680 --> 00:23:54,400 Yeah. Thank you for sharing your for ease I 641 00:23:54,400 --> 00:23:57,160 love frameworks so that is that is that is 642 00:23:57,160 --> 00:23:58,920 definitely right up my alley. So thank you 643 00:23:58,920 --> 00:24:01,160 for that. So, uh, let me ask you this, 644 00:24:01,160 --> 00:24:03,360 Debbie. So when we think about especially a 645 00:24:03,360 --> 00:24:05,880 lot of the, the clients that you work with or 646 00:24:05,880 --> 00:24:08,120 just people who who come to you or who are in 647 00:24:08,120 --> 00:24:09,880 your world, the scientists and the engineer 648 00:24:09,880 --> 00:24:11,680 and the health professionals, uh, 649 00:24:12,400 --> 00:24:14,360 obviously, and very rightly so. 650 00:24:14,400 --> 00:24:17,410 They they are very deep into the details of 651 00:24:17,450 --> 00:24:19,170 their research. You know, oftentimes they 652 00:24:19,170 --> 00:24:20,970 spend years studying it. 653 00:24:20,970 --> 00:24:22,290 They're doing research. They're putting 654 00:24:22,330 --> 00:24:24,730 together the data and the analysis to draw 655 00:24:24,730 --> 00:24:26,090 conclusions from it. 656 00:24:26,090 --> 00:24:27,970 So it's very precious to them. 657 00:24:28,010 --> 00:24:30,730 Plus, they are very familiar with it, 658 00:24:30,730 --> 00:24:34,210 with all the jargon, the vocabulary way, 659 00:24:34,330 --> 00:24:37,050 the conversations that their research is a 660 00:24:37,050 --> 00:24:39,770 part of. But then you go to either the public 661 00:24:39,770 --> 00:24:42,250 or to policymakers, and they don't have that 662 00:24:42,250 --> 00:24:43,770 familiarity at all. 663 00:24:43,810 --> 00:24:45,650 I remember we were working with some faculty 664 00:24:45,650 --> 00:24:47,450 at the University of California, 665 00:24:47,730 --> 00:24:49,970 and this one woman, she has a PhD in 666 00:24:49,970 --> 00:24:52,090 engineering, and she was trying to do. 667 00:24:52,130 --> 00:24:54,250 She was talking about cement and concrete and 668 00:24:54,250 --> 00:24:57,730 making it more environmentally friendly and 669 00:24:57,730 --> 00:25:00,410 how to do that. And so I had to I had to help 670 00:25:00,410 --> 00:25:02,090 her find a story to do that, 671 00:25:02,090 --> 00:25:03,890 because cement and concrete, 672 00:25:04,010 --> 00:25:06,010 like, we don't know, like those of us who 673 00:25:06,050 --> 00:25:07,890 don't work in that, that's not our thing. 674 00:25:07,890 --> 00:25:10,250 So when you're thinking about the people that 675 00:25:10,250 --> 00:25:12,930 you work with, how do you help them to kind 676 00:25:12,970 --> 00:25:15,700 of pull their head out of the really that 677 00:25:15,700 --> 00:25:17,780 minutiae and get them to see the bigger 678 00:25:17,780 --> 00:25:19,980 bird's eye view, to translate that to the 679 00:25:19,980 --> 00:25:21,900 public and to policy makers. 680 00:25:22,780 --> 00:25:25,300 Deborah Stine: Well, the first thing is I really work on 681 00:25:25,300 --> 00:25:27,980 them getting a good headline, 682 00:25:28,020 --> 00:25:30,460 right? So, you know, like, 683 00:25:31,060 --> 00:25:32,740 imagine I know, you know, 684 00:25:32,780 --> 00:25:35,020 like, like how to do keynotes and these other 685 00:25:35,020 --> 00:25:36,540 things. Right. Okay. 686 00:25:36,580 --> 00:25:39,740 Probably I, I suspect, 687 00:25:39,900 --> 00:25:42,260 um, that one of the things you do is you need 688 00:25:42,260 --> 00:25:43,540 a good headline for your talk. 689 00:25:43,580 --> 00:25:45,980 You need something that will grab people's 690 00:25:45,980 --> 00:25:49,060 attention and bring them in and do these 691 00:25:49,060 --> 00:25:51,060 things. So when I'm teaching, 692 00:25:51,100 --> 00:25:52,580 like, in fact, I'm going to be at the 693 00:25:52,580 --> 00:25:55,340 University of Virginia, like in about a 694 00:25:55,340 --> 00:25:58,060 month, actually doing this workshop with 695 00:25:58,100 --> 00:25:59,220 University of Virginia, 696 00:25:59,340 --> 00:26:01,580 um, uh, graduate students. 697 00:26:01,660 --> 00:26:04,900 Uh, and I will work with them over the course 698 00:26:04,900 --> 00:26:06,460 of a day to get it. 699 00:26:06,460 --> 00:26:09,380 So whatever their key messages, 700 00:26:09,580 --> 00:26:11,940 they can get it down to initially, 701 00:26:11,940 --> 00:26:15,590 that one Sentence that headline the things 702 00:26:15,630 --> 00:26:17,950 you know, like the way I explain it is that, 703 00:26:17,990 --> 00:26:20,030 you know, like when you when they themselves. 704 00:26:20,030 --> 00:26:22,030 Right, they're looking through, 705 00:26:22,150 --> 00:26:23,270 say an online right. 706 00:26:23,310 --> 00:26:25,390 And they're, you know, maybe whatever it is, 707 00:26:25,390 --> 00:26:27,430 whether it's like they're on LinkedIn or 708 00:26:27,430 --> 00:26:29,950 they're on some, uh, I don't know, 709 00:26:30,310 --> 00:26:32,350 through Facebook or just a, 710 00:26:32,510 --> 00:26:34,590 their journal, it doesn't matter what it is. 711 00:26:34,790 --> 00:26:37,590 They're looking right at those headlines, 712 00:26:37,590 --> 00:26:40,150 those titles, and they're saying, 713 00:26:40,510 --> 00:26:43,310 should I read or listen to this or should I 714 00:26:43,350 --> 00:26:45,070 not? Right. 715 00:26:45,590 --> 00:26:48,590 And that that's really the first key is 716 00:26:48,590 --> 00:26:50,590 getting it so they can get it down to that 717 00:26:50,590 --> 00:26:53,910 one sentence, the one thing they want people 718 00:26:53,910 --> 00:26:56,270 to remember and they want people to do 719 00:26:56,310 --> 00:26:57,590 differently. Right. 720 00:26:57,910 --> 00:26:59,630 So you're not just saying, 721 00:26:59,910 --> 00:27:03,230 um, like nurses are important or something 722 00:27:03,230 --> 00:27:05,230 like that. You're saying, 723 00:27:05,630 --> 00:27:12,800 you know, change, um, the policy of nurse of 724 00:27:13,400 --> 00:27:15,960 those getting nursing degrees to make it a 725 00:27:15,960 --> 00:27:17,360 professional, you know, 726 00:27:17,400 --> 00:27:19,200 degree. Right. 727 00:27:19,240 --> 00:27:20,720 So you're saying very, 728 00:27:20,720 --> 00:27:23,160 very clear. What is you want them to change. 729 00:27:23,160 --> 00:27:26,120 And it can only be one thing. 730 00:27:26,160 --> 00:27:28,880 Yes. It cannot be 500 things. 731 00:27:28,920 --> 00:27:32,000 Right. So when I teach them analysis right, 732 00:27:32,040 --> 00:27:33,880 I say okay, you have to first start with the 733 00:27:33,880 --> 00:27:35,960 status quo. That status quo is the current 734 00:27:35,960 --> 00:27:38,280 policy. And then you look at two different 735 00:27:38,280 --> 00:27:39,640 policy options. 736 00:27:39,640 --> 00:27:43,160 And I say in the end you have to pick only 737 00:27:43,160 --> 00:27:45,200 one. That's it. 738 00:27:45,440 --> 00:27:47,400 You cannot combine the two together. 739 00:27:47,520 --> 00:27:49,320 You cannot do anything else. 740 00:27:49,320 --> 00:27:50,960 You got to pick one. 741 00:27:50,960 --> 00:27:53,360 And the one that you pick needs to be based 742 00:27:53,360 --> 00:27:54,680 on analysis. 743 00:27:54,680 --> 00:27:56,320 It needs to be based on evidence, 744 00:27:56,520 --> 00:27:58,200 not just your opinion. 745 00:27:58,240 --> 00:28:00,320 Oh, I think we should do solar. 746 00:28:00,320 --> 00:28:01,400 I think we should do wind. 747 00:28:01,400 --> 00:28:04,200 Well, you know, solar doesn't necessarily 748 00:28:04,200 --> 00:28:06,400 make sense where I am here in Pittsburgh, 749 00:28:06,720 --> 00:28:09,440 you know. Yet it might also make sense in 750 00:28:09,440 --> 00:28:10,760 Arizona, right? 751 00:28:10,800 --> 00:28:13,120 So I teach them, you know, 752 00:28:13,160 --> 00:28:17,320 how to really get down to that one sentence I 753 00:28:17,320 --> 00:28:20,240 teach and say, hey, you got to find a story. 754 00:28:20,240 --> 00:28:22,800 So I teach them how to find a story, 755 00:28:23,360 --> 00:28:25,480 you know, or something that they can use that 756 00:28:25,480 --> 00:28:28,200 they can, like, um, bring several stories 757 00:28:28,200 --> 00:28:30,800 together, you know, just like a author does, 758 00:28:31,040 --> 00:28:33,880 to kind of illustrate what they need to do. 759 00:28:34,240 --> 00:28:36,880 And then somehow they need to also connect it 760 00:28:36,880 --> 00:28:38,840 to whomever it is they're speaking, 761 00:28:38,840 --> 00:28:41,160 the policy maker to whom they're speaking. 762 00:28:41,360 --> 00:28:44,080 Right. And that means understanding them, 763 00:28:44,120 --> 00:28:46,360 understanding their their jurisdiction, 764 00:28:46,360 --> 00:28:48,480 what it is they have control over and not 765 00:28:48,680 --> 00:28:50,160 have control over, you know, 766 00:28:50,200 --> 00:28:52,200 so the governor of Pennsylvania, 767 00:28:52,200 --> 00:28:55,160 for example, can't make policy for new 768 00:28:55,160 --> 00:28:56,440 Jersey, right? 769 00:28:56,760 --> 00:28:59,720 Um, and the governor of Pennsylvania, 770 00:28:59,760 --> 00:29:01,480 you know, he can only do so much on, 771 00:29:01,520 --> 00:29:04,320 say, Stem education because Stem education is 772 00:29:04,320 --> 00:29:06,000 generally a local issue. 773 00:29:06,160 --> 00:29:09,250 Right? And so part it's no good to tell a 774 00:29:09,250 --> 00:29:12,650 policymaker to do things that they legally 775 00:29:12,650 --> 00:29:14,690 cannot do. Right. 776 00:29:14,930 --> 00:29:17,730 Um, it needs to be something that relates to 777 00:29:17,770 --> 00:29:20,290 their constituency and to their, 778 00:29:20,490 --> 00:29:24,210 um, um, their what it is that they can do, 779 00:29:24,570 --> 00:29:26,330 uh, illegally. 780 00:29:26,490 --> 00:29:29,010 Uh, and also that kind of relates to their 781 00:29:29,010 --> 00:29:31,370 background and their interest. 782 00:29:31,570 --> 00:29:33,170 You know, if somebody is like, 783 00:29:33,290 --> 00:29:35,370 not at all interested in, 784 00:29:36,370 --> 00:29:38,370 I don't know, climate change, 785 00:29:38,730 --> 00:29:40,570 you know, it's no good trying to convince 786 00:29:40,570 --> 00:29:42,770 that person to do something on climate 787 00:29:42,770 --> 00:29:46,010 change. Instead, you have to take a different 788 00:29:46,010 --> 00:29:49,090 route. So, uh, like one example, 789 00:29:49,130 --> 00:29:50,730 like in this region, um, 790 00:29:50,730 --> 00:29:52,330 in fact, there's an article I think maybe in 791 00:29:52,330 --> 00:29:53,770 the New York Times, uh, 792 00:29:53,770 --> 00:29:55,970 about flooding. So flooding in Appalachia is 793 00:29:55,970 --> 00:29:57,090 a really big issue. 794 00:29:57,410 --> 00:29:59,530 So you don't talk to, say, 795 00:29:59,530 --> 00:30:02,170 a West Virginia policymaker about climate 796 00:30:02,170 --> 00:30:05,170 change. Instead you talk to them about 797 00:30:05,170 --> 00:30:06,490 flooding. Mhm. 798 00:30:06,940 --> 00:30:09,260 Uh, you talk to them about the impact, 799 00:30:09,260 --> 00:30:11,020 the economic impact of flooding. 800 00:30:11,060 --> 00:30:13,020 About the impact on businesses and on 801 00:30:13,020 --> 00:30:15,420 community things that they care about. 802 00:30:15,460 --> 00:30:17,180 Right. They care about West Virginians. They 803 00:30:17,180 --> 00:30:18,900 care about about the economy. 804 00:30:19,580 --> 00:30:21,540 Don't try to convince them that climate 805 00:30:21,540 --> 00:30:23,020 change is causing flooding, 806 00:30:23,020 --> 00:30:24,620 although it is. Right. 807 00:30:24,660 --> 00:30:26,460 There's, you know, there's there's plenty of 808 00:30:26,460 --> 00:30:28,700 data for that, that that's not what you want 809 00:30:28,740 --> 00:30:31,220 to focus on. Instead, you focus on the end 810 00:30:31,260 --> 00:30:34,340 goal, which is hopefully getting West 811 00:30:34,340 --> 00:30:37,500 Virginia, West Virginia to invest more in 812 00:30:37,500 --> 00:30:39,860 flooding, uh, protection, 813 00:30:40,340 --> 00:30:42,820 uh, because it will help their people, 814 00:30:43,220 --> 00:30:44,900 not because of climate change. 815 00:30:44,940 --> 00:30:47,500 Carol Cox: Right. It reminds me of the old saying, 816 00:30:48,220 --> 00:30:49,700 do you want to be effective or do you want to 817 00:30:49,700 --> 00:30:50,300 be right? 818 00:30:50,700 --> 00:30:52,340 Deborah Stine: There you go. That's a good way of putting 819 00:30:52,340 --> 00:30:53,740 it. Yes. Do you want to be effective or do 820 00:30:53,740 --> 00:30:54,860 you want to be right? Yeah. 821 00:30:54,900 --> 00:30:57,220 Yes. And and so part one of the workshops I 822 00:30:57,220 --> 00:30:59,260 teach is how to interact with people with 823 00:30:59,260 --> 00:31:00,340 whom you disagree. 824 00:31:00,940 --> 00:31:02,420 Um, and this is an example. 825 00:31:02,420 --> 00:31:04,660 And part of it is just first being 826 00:31:04,790 --> 00:31:07,670 respectful, you know, and not talking down to 827 00:31:07,670 --> 00:31:10,150 them, not criticizing them because they don't 828 00:31:10,150 --> 00:31:12,150 believe that climate change is happening, 829 00:31:12,150 --> 00:31:13,910 all these different things. You know, 830 00:31:13,950 --> 00:31:15,070 that's the first thing. 831 00:31:15,110 --> 00:31:16,470 The second is listening. 832 00:31:16,510 --> 00:31:17,590 You know, you really need to be a good 833 00:31:17,590 --> 00:31:19,870 listener, make a connection with them. 834 00:31:19,910 --> 00:31:21,390 You know, everybody can make some connection. 835 00:31:21,390 --> 00:31:23,070 Maybe it's because you both have dogs. 836 00:31:23,070 --> 00:31:25,710 Maybe it's, you know, because you know, 837 00:31:25,990 --> 00:31:28,310 the, you know, you watch the same football 838 00:31:28,310 --> 00:31:29,990 teams or whatever, whatever it is, 839 00:31:30,430 --> 00:31:33,150 you need to somehow make a connection, 840 00:31:33,150 --> 00:31:34,590 like with that person, 841 00:31:35,070 --> 00:31:38,550 before you really kind of dive in to whatever 842 00:31:38,550 --> 00:31:40,670 it is that you are there to talk about. 843 00:31:40,710 --> 00:31:42,750 And I understand this is challenging. Back 844 00:31:42,750 --> 00:31:44,350 when I was a field engineer, 845 00:31:44,350 --> 00:31:48,470 I had a boss who was a former NASA engineer. 846 00:31:48,510 --> 00:31:50,510 Right. But he was head of this regional 847 00:31:50,510 --> 00:31:52,430 office, uh, that I was part of. 848 00:31:52,430 --> 00:31:55,150 This is for the Texas Air Pollution Agency. 849 00:31:55,630 --> 00:31:57,990 And, you know, I would go and I would give a 850 00:31:57,990 --> 00:31:59,950 violation to some company because they 851 00:31:59,950 --> 00:32:01,390 violated some air pollution laws, and they 852 00:32:01,390 --> 00:32:03,720 would come in for a meeting and he would 853 00:32:03,720 --> 00:32:06,760 spin, I swear, the first 15 20 minutes 854 00:32:06,800 --> 00:32:09,000 talking about football. 855 00:32:09,480 --> 00:32:11,200 I don't know the weather. 856 00:32:11,240 --> 00:32:13,640 I don't know. It just drove me crazy. 857 00:32:14,480 --> 00:32:16,360 And then we would finally get to it. 858 00:32:16,520 --> 00:32:19,040 I did not understand that when I was like 25 859 00:32:19,040 --> 00:32:20,120 years old. Right. 860 00:32:20,400 --> 00:32:21,880 I now understand it. 861 00:32:21,920 --> 00:32:26,440 Right. And we are not taught in the Stem 862 00:32:26,440 --> 00:32:28,800 world how to communicate with other people. 863 00:32:29,200 --> 00:32:31,240 We should, um, you know, 864 00:32:31,280 --> 00:32:32,720 or maybe it should be part of our general 865 00:32:32,720 --> 00:32:34,800 education. Whatever. 866 00:32:35,000 --> 00:32:36,680 Um, I went to University of California for 867 00:32:36,680 --> 00:32:39,440 engineering school. I wasn't taught any of 868 00:32:39,480 --> 00:32:42,320 that, even though I did learn a lot about 869 00:32:42,320 --> 00:32:44,160 literature and other things. 870 00:32:44,560 --> 00:32:47,880 But I was not taught how to communicate about 871 00:32:48,360 --> 00:32:49,880 anything related to Stem. 872 00:32:50,440 --> 00:32:54,120 Carol Cox: Yes. And I feel like that is most industries 873 00:32:54,160 --> 00:32:55,960 even I mean Stem specifically, 874 00:32:55,960 --> 00:32:57,760 but even so many other ones and, 875 00:32:58,000 --> 00:32:59,920 uh, you know, so I've been involved in 876 00:32:59,920 --> 00:33:02,930 Democratic politics for over 20 years now. 877 00:33:03,250 --> 00:33:05,770 And, you know, I and I know Debbie, 878 00:33:05,770 --> 00:33:07,210 you don't do politics. 879 00:33:07,210 --> 00:33:08,970 I you're on the policy side and I'm on on the 880 00:33:08,970 --> 00:33:10,770 politics side. But I will say that, 881 00:33:11,130 --> 00:33:12,490 you know, seeing what has happened with the 882 00:33:12,490 --> 00:33:14,410 Democratic Party specifically in the past 883 00:33:14,410 --> 00:33:16,810 year with last year's election and then what 884 00:33:16,810 --> 00:33:19,090 happened earlier this year and inability to 885 00:33:19,090 --> 00:33:21,890 stand up to the Trump administration is that 886 00:33:22,090 --> 00:33:23,890 I feel like and this is my own little 887 00:33:23,890 --> 00:33:25,490 separate soapbox rant. 888 00:33:25,490 --> 00:33:26,970 You don't have to comment on this unless you 889 00:33:26,970 --> 00:33:30,010 want to. Is that the and I'm including myself 890 00:33:30,010 --> 00:33:32,570 in this is that the Democrats got so fixated 891 00:33:32,570 --> 00:33:35,730 on purity test for making sure that every 892 00:33:35,730 --> 00:33:38,850 person checked every single box for whatever 893 00:33:38,890 --> 00:33:40,330 the different issues were, 894 00:33:40,490 --> 00:33:42,730 rather than figuring out how can we connect 895 00:33:42,730 --> 00:33:45,370 with voters so that they understand that we 896 00:33:45,530 --> 00:33:47,090 are listening to them and we're on their 897 00:33:47,090 --> 00:33:48,570 side, and we want to help them, 898 00:33:48,570 --> 00:33:50,650 and we want them to understand how to help 899 00:33:50,650 --> 00:33:53,410 themselves, like to aspire to that American 900 00:33:53,410 --> 00:33:55,610 dream. And we totally took our eye off the 901 00:33:55,610 --> 00:33:57,970 ball. And hence this is where we're at. 902 00:33:57,970 --> 00:34:00,180 But I'm thinking, based on November's 903 00:34:00,180 --> 00:34:03,420 elections, and now we're getting back into 904 00:34:03,420 --> 00:34:05,660 the right frame of mind for next year in the 905 00:34:05,660 --> 00:34:06,260 midterms. 906 00:34:06,420 --> 00:34:07,980 Deborah Stine: Yeah, no, but I agree with you. Yes. 907 00:34:07,980 --> 00:34:09,300 No, I totally agree. 908 00:34:09,300 --> 00:34:11,540 And, you know, even, um, 909 00:34:12,540 --> 00:34:14,140 you know, in fact, I was just actually 910 00:34:14,500 --> 00:34:15,780 mentioning this to somebody. I think like 911 00:34:15,820 --> 00:34:19,060 yesterday there was a there was a certain 912 00:34:19,100 --> 00:34:21,620 kind of like, holier than thou kind of 913 00:34:21,660 --> 00:34:27,260 mentality, um, that that sort of pervaded, 914 00:34:27,620 --> 00:34:29,140 uh, or still pervades in some ways, 915 00:34:29,180 --> 00:34:32,020 you know, like, uh, a good portion of the 916 00:34:32,020 --> 00:34:33,180 Democratic Party. 917 00:34:33,180 --> 00:34:35,980 And it's making, you know, 918 00:34:36,020 --> 00:34:37,420 as opposed to saying we, 919 00:34:37,460 --> 00:34:40,260 you know, we are it's us. 920 00:34:40,260 --> 00:34:41,940 Right? Like, you know, we're all like 921 00:34:41,980 --> 00:34:43,660 Americans and things like that. 922 00:34:43,660 --> 00:34:45,740 But thinking that we're better than other 923 00:34:45,740 --> 00:34:49,060 people or all these other kinds of things, 924 00:34:49,060 --> 00:34:52,020 it just is, is a losing proposition. 925 00:34:52,300 --> 00:34:54,100 And, uh, you know, just as, 926 00:34:54,300 --> 00:34:55,980 as basically the same thing, as I said, with 927 00:34:55,980 --> 00:34:57,500 the people with whom you disagree. 928 00:34:57,500 --> 00:34:59,380 Three. Well, you know, 929 00:34:59,420 --> 00:35:01,460 if if people don't feel that you have, 930 00:35:01,860 --> 00:35:03,660 um, that you respect them, 931 00:35:04,180 --> 00:35:06,140 then, you know, they're going to feel, 932 00:35:06,140 --> 00:35:08,340 hey, you're not not going to listen to me. 933 00:35:08,620 --> 00:35:10,180 One of the big changes, 934 00:35:10,580 --> 00:35:12,820 um, and this was actually a very good thing 935 00:35:12,820 --> 00:35:14,660 that happened during the Biden administration 936 00:35:14,940 --> 00:35:18,220 was in the scientific world is a memo came 937 00:35:18,220 --> 00:35:20,380 out from HHS, the Department of Health and 938 00:35:20,380 --> 00:35:22,980 Human Services about honoring what they call 939 00:35:23,020 --> 00:35:24,540 lived experiences. 940 00:35:25,420 --> 00:35:27,420 And so what that means is that, 941 00:35:27,820 --> 00:35:29,540 uh, when you are having, 942 00:35:29,700 --> 00:35:31,540 uh, like a conversation with somebody like, 943 00:35:31,580 --> 00:35:32,980 let's just talk about flooding in West 944 00:35:32,980 --> 00:35:34,940 Virginia, right? Well, 945 00:35:34,940 --> 00:35:37,660 we can have all the data that we want about 946 00:35:37,660 --> 00:35:39,060 flooding, right? 947 00:35:39,100 --> 00:35:42,780 But equally important is not just a 948 00:35:42,780 --> 00:35:44,860 scientific, quantitative data, 949 00:35:44,860 --> 00:35:46,620 but the lived experiences of the West 950 00:35:46,620 --> 00:35:48,060 Virginians who have lived in these 951 00:35:48,060 --> 00:35:50,100 communities for like, you know, 952 00:35:50,140 --> 00:35:51,420 maybe their family has lived in their 953 00:35:51,460 --> 00:35:52,700 hundreds of years, right? 954 00:35:52,940 --> 00:35:56,590 They know the flooding when it happens. 955 00:35:56,590 --> 00:35:58,710 Why it happens. All those different things. 956 00:35:59,150 --> 00:36:00,910 And so what this memo, 957 00:36:01,030 --> 00:36:04,990 um, did was say, hey, we need to incorporate 958 00:36:04,990 --> 00:36:08,510 it. And it is important as lived experiences. 959 00:36:08,550 --> 00:36:11,470 Now, the challenge that occurred is that, 960 00:36:11,510 --> 00:36:13,070 you know, I talk about equity. 961 00:36:13,110 --> 00:36:14,950 Equity is one of the fundamental principles 962 00:36:14,950 --> 00:36:16,750 of policy analysis, the fairness of the 963 00:36:16,750 --> 00:36:17,990 policy, things like that. 964 00:36:18,150 --> 00:36:22,110 The word equity just got focused so much on 965 00:36:22,110 --> 00:36:26,070 Dei that the concept of equity as fairness, 966 00:36:26,070 --> 00:36:29,150 which is what it means in policy analysis and 967 00:36:29,390 --> 00:36:32,110 and saying that we need to be fair and honor 968 00:36:32,270 --> 00:36:33,630 this kind of knowledge, 969 00:36:33,870 --> 00:36:34,990 it got lost. 970 00:36:35,190 --> 00:36:39,270 Right. Um, and so that like one of the, 971 00:36:39,310 --> 00:36:41,110 one of the things I teach is equity analysis. 972 00:36:41,110 --> 00:36:42,990 I have for a really, really long time. 973 00:36:43,470 --> 00:36:47,070 And, uh, it really, really is understanding 974 00:36:47,470 --> 00:36:49,630 that fairness, understanding, 975 00:36:50,070 --> 00:36:52,270 you know, and involving that community. 976 00:36:52,310 --> 00:36:54,070 And that's one of the things that I, 977 00:36:54,110 --> 00:36:56,720 um, like, I did a whole like sort of five 978 00:36:56,720 --> 00:36:59,200 part webinar series, uh, 979 00:36:59,200 --> 00:37:01,720 for an organization called Innovate Us that 980 00:37:01,720 --> 00:37:03,760 talked about community engagement and 981 00:37:03,800 --> 00:37:06,760 honoring that knowledge when one of the 982 00:37:06,760 --> 00:37:08,360 things when I was in Africa, 983 00:37:09,080 --> 00:37:11,400 um, I met a guy and he said, 984 00:37:11,400 --> 00:37:14,360 well, you know, um, I just, 985 00:37:14,400 --> 00:37:17,000 you know, I just don't know as much. 986 00:37:17,160 --> 00:37:18,800 You know, I don't I never went to school. 987 00:37:18,840 --> 00:37:20,480 Right? And I said, look, 988 00:37:20,520 --> 00:37:22,760 just because you didn't go to school doesn't 989 00:37:22,760 --> 00:37:24,720 mean that you don't have a lot of knowledge. 990 00:37:24,880 --> 00:37:27,000 It's just it's learned knowledge. 991 00:37:27,040 --> 00:37:28,920 It's learned in real life. 992 00:37:29,120 --> 00:37:32,200 Right. Um, but yeah, it is, 993 00:37:32,360 --> 00:37:34,680 you know, and I think it's sort of even as 994 00:37:34,680 --> 00:37:39,200 we, um, you know, go into this like the next 995 00:37:39,200 --> 00:37:42,080 election, you know, it's still not a done 996 00:37:42,080 --> 00:37:44,600 deal, right? Because we still do have 997 00:37:45,000 --> 00:37:47,240 definite, uh, challenges, 998 00:37:47,520 --> 00:37:50,960 um, in, in, um, you know, 999 00:37:51,000 --> 00:37:53,850 interacting with real people and honoring 1000 00:37:53,850 --> 00:37:56,650 them regardless of, you know, 1001 00:37:56,690 --> 00:37:58,970 their status in terms of, 1002 00:37:59,570 --> 00:38:01,850 um, you know, their, you know, 1003 00:38:01,890 --> 00:38:05,570 assuming that you're better than sorry, 1004 00:38:05,850 --> 00:38:07,690 that you're better than them for any of these 1005 00:38:07,690 --> 00:38:08,770 other purposes. 1006 00:38:09,090 --> 00:38:14,050 Um, and it's a very frustrating thing for me, 1007 00:38:14,090 --> 00:38:15,930 you know, to do, particularly since I was a 1008 00:38:15,930 --> 00:38:17,450 field engineer for five years, 1009 00:38:17,490 --> 00:38:19,330 I spent a lot of time on the road talking to 1010 00:38:19,370 --> 00:38:23,570 people. Um, you know, we really just need to, 1011 00:38:23,810 --> 00:38:27,610 to, uh, to try to have more engagement. 1012 00:38:27,970 --> 00:38:30,210 Uh, but it's it's a real challenge because 1013 00:38:30,210 --> 00:38:32,210 everybody's busy, including the people with 1014 00:38:32,210 --> 00:38:33,330 whom we want to engage. 1015 00:38:33,330 --> 00:38:35,130 Right. And then the feeling is, 1016 00:38:35,170 --> 00:38:36,930 okay, you're going to talk to them or try to 1017 00:38:36,930 --> 00:38:39,650 convince them as opposed to listen to what it 1018 00:38:39,650 --> 00:38:40,530 is they have to say. 1019 00:38:40,970 --> 00:38:42,210 Carol Cox: Yes. All right. Very well, 1020 00:38:42,210 --> 00:38:45,610 said Debbie. So I'm going to make a 90 degree 1021 00:38:45,610 --> 00:38:47,650 turn here, and we're going to talk about AI, 1022 00:38:47,930 --> 00:38:50,050 which of course is still related to science 1023 00:38:50,050 --> 00:38:52,620 and technology. It's just a different aspect 1024 00:38:52,620 --> 00:38:56,740 of it. So we actually met because we have a 1025 00:38:56,740 --> 00:38:58,060 mutual connection. 1026 00:38:58,060 --> 00:39:01,060 And she had gone through my automated amplify 1027 00:39:01,060 --> 00:39:03,700 with AI program that I ran over the summer, 1028 00:39:03,900 --> 00:39:05,180 and it was a lot of fun. 1029 00:39:05,180 --> 00:39:08,140 And I taught a bunch of AI automations for 1030 00:39:08,180 --> 00:39:09,500 content creation. 1031 00:39:09,500 --> 00:39:11,980 So she mentioned must you must have been 1032 00:39:11,980 --> 00:39:13,460 talking with with Stephanie, 1033 00:39:13,460 --> 00:39:15,300 and she mentioned the program to you. 1034 00:39:15,300 --> 00:39:16,500 So you reached out. 1035 00:39:16,500 --> 00:39:18,860 And so then I gave you access to the 1036 00:39:18,860 --> 00:39:21,380 self-paced version of the program, 1037 00:39:21,380 --> 00:39:23,180 because you were going on your trip to 1038 00:39:23,220 --> 00:39:25,140 Africa, and you very much wanted to keep up 1039 00:39:25,140 --> 00:39:27,180 with your LinkedIn newsletter and some of 1040 00:39:27,180 --> 00:39:28,660 your other content creation. 1041 00:39:28,660 --> 00:39:31,700 And so, voila, AI automations came to the 1042 00:39:31,700 --> 00:39:34,060 rescue. So can you tell us a little bit about 1043 00:39:34,380 --> 00:39:37,140 what what you did with those and what you 1044 00:39:37,140 --> 00:39:38,380 found? The benefits were? 1045 00:39:39,060 --> 00:39:42,540 Deborah Stine: Yeah. So Stephanie is somebody she her 1046 00:39:42,540 --> 00:39:44,580 organization is called Rising Engineers. 1047 00:39:45,140 --> 00:39:47,700 And I've known her for a good number of 1048 00:39:47,740 --> 00:39:49,710 years, I guess, I guess maybe five years ago 1049 00:39:49,710 --> 00:39:52,190 we were part of a, like an educational 1050 00:39:52,190 --> 00:39:54,190 activity together for quite a while. 1051 00:39:54,550 --> 00:39:56,830 Um, and we were just kind of chit chatting, 1052 00:39:56,870 --> 00:39:58,670 updating, and she said, 1053 00:39:58,670 --> 00:40:00,390 oh, you know, I went through this thing and 1054 00:40:00,390 --> 00:40:02,430 so forth, and it was like the best $500 I've 1055 00:40:02,430 --> 00:40:05,470 ever spent. I said, oh, really? And I myself, 1056 00:40:05,550 --> 00:40:07,430 uh, have been doing a lot on AI. 1057 00:40:07,470 --> 00:40:10,350 So I've been doing analysis on AI data 1058 00:40:10,390 --> 00:40:12,590 centers. I have a whole series of webinars 1059 00:40:12,590 --> 00:40:14,030 that I've done on them from, 1060 00:40:14,350 --> 00:40:18,150 um, like an environmental and manufacturing 1061 00:40:18,190 --> 00:40:19,390 kind of standpoint. 1062 00:40:19,630 --> 00:40:22,790 Um, and then, uh, I also teach things about 1063 00:40:22,830 --> 00:40:26,910 AI. Uh, had had applied DNI for policy 1064 00:40:26,910 --> 00:40:28,310 analysis, program evaluation, 1065 00:40:28,310 --> 00:40:30,470 all the different things that I teach, 1066 00:40:30,830 --> 00:40:32,430 but I had never really, 1067 00:40:32,630 --> 00:40:34,470 uh, and this has happened to me before. I 1068 00:40:34,510 --> 00:40:36,710 never really thought about applying it that 1069 00:40:36,710 --> 00:40:39,110 much to my own, uh, business. 1070 00:40:39,150 --> 00:40:42,270 Right. And so, um, I went, 1071 00:40:42,310 --> 00:40:44,350 um, you know, to your website, 1072 00:40:44,350 --> 00:40:46,190 and I watched the video and I thought, 1073 00:40:46,190 --> 00:40:49,070 oh, that that could really kind of help me do 1074 00:40:49,070 --> 00:40:50,990 these things. And what I liked about it was 1075 00:40:50,990 --> 00:40:53,830 the concept of, of I don't want like, 1076 00:40:53,830 --> 00:40:55,790 want to produce things that are generic. 1077 00:40:55,910 --> 00:40:57,790 Right. So a lot of one of the things I'm 1078 00:40:57,870 --> 00:40:59,550 doing, because a lot of scientists and 1079 00:40:59,550 --> 00:41:01,070 engineers and health professionals out there 1080 00:41:01,070 --> 00:41:02,670 looking for jobs right now because of the 1081 00:41:02,670 --> 00:41:05,430 Trump administration, and it's unexpected for 1082 00:41:05,430 --> 00:41:07,150 them. They thought they were in their dream 1083 00:41:07,150 --> 00:41:08,470 jobs. They thought they'd be there for the 1084 00:41:08,470 --> 00:41:10,550 rest of their lives. And so I have a lot of 1085 00:41:10,550 --> 00:41:13,510 people, you know, that I'm trying to help. 1086 00:41:13,830 --> 00:41:16,110 Um, and I don't don't want to, 1087 00:41:16,150 --> 00:41:18,070 like, not help them while I go on my 1088 00:41:18,070 --> 00:41:20,550 adventures. All of which is, 1089 00:41:20,550 --> 00:41:22,190 as I say, were playing pre-trump. 1090 00:41:22,230 --> 00:41:24,550 Right. Um, and so I do. 1091 00:41:24,710 --> 00:41:26,710 Um, so I actually produce for LinkedIn 1092 00:41:26,710 --> 00:41:28,790 newsletters. One is like an opinion 1093 00:41:28,790 --> 00:41:31,430 newsletter. Uh, one is a jobs newsletter. 1094 00:41:31,430 --> 00:41:32,910 So it's for people who are looking for 1095 00:41:32,910 --> 00:41:34,430 science, technology, policy jobs. 1096 00:41:34,430 --> 00:41:37,670 And so I have over like 100 listings every 1097 00:41:37,670 --> 00:41:39,510 week that I collect from LinkedIn. 1098 00:41:39,830 --> 00:41:43,150 Um, and then, um, I have uh, 1099 00:41:43,590 --> 00:41:47,280 one which is, uh, About my sort of like about 1100 00:41:47,280 --> 00:41:48,480 my book. It's about it's kind of like 1101 00:41:48,520 --> 00:41:50,360 science, technology policy 101. 1102 00:41:50,640 --> 00:41:52,720 But the one that I really wanted to do, 1103 00:41:52,920 --> 00:41:56,720 keep up, you know, while I was gone was what 1104 00:41:56,720 --> 00:41:58,400 I call was, was Dear Debbie. 1105 00:41:58,400 --> 00:42:01,400 So Dear Debbie is where people send in 1106 00:42:01,400 --> 00:42:04,920 questions like, um, anything that sort of 1107 00:42:04,960 --> 00:42:07,200 career related like, well, 1108 00:42:07,760 --> 00:42:10,400 what is this ATS resume thing? 1109 00:42:10,400 --> 00:42:12,000 And like, how do I do it? 1110 00:42:12,000 --> 00:42:13,880 Or the one that's actually coming out 1111 00:42:13,880 --> 00:42:17,240 tomorrow is, you know what, 1112 00:42:17,280 --> 00:42:19,520 what should I do during the holiday season? 1113 00:42:19,760 --> 00:42:21,400 Um, you know, should I keep looking for a 1114 00:42:21,400 --> 00:42:23,160 job? What what should I do? 1115 00:42:23,240 --> 00:42:25,600 All these different things. Right. So when I 1116 00:42:25,600 --> 00:42:28,760 produce these, um, I want to, 1117 00:42:29,720 --> 00:42:31,680 you know, answer them with things that are 1118 00:42:31,720 --> 00:42:33,160 in, like, my voice. 1119 00:42:33,200 --> 00:42:36,200 Right? And so that's what really appealed to 1120 00:42:36,200 --> 00:42:38,600 me. So, um, what I did is, 1121 00:42:38,600 --> 00:42:40,560 is when I, as I was trying to figure this 1122 00:42:40,560 --> 00:42:41,760 out, I took. 1123 00:42:42,000 --> 00:42:44,170 I have a lot of Out of content. 1124 00:42:44,530 --> 00:42:46,970 Um, that is, you know, 1125 00:42:47,010 --> 00:42:50,090 because of my book, because of all this, 1126 00:42:50,130 --> 00:42:52,170 all these, uh, posts and things that have 1127 00:42:52,170 --> 00:42:53,770 been generating. I have all the, 1128 00:42:54,090 --> 00:42:55,770 the workshops I give, like I said, 1129 00:42:55,810 --> 00:42:58,050 like 40 different workshops, as you know, 1130 00:42:58,090 --> 00:42:59,610 all these different things. So I have all 1131 00:42:59,610 --> 00:43:00,810 that content. 1132 00:43:00,810 --> 00:43:01,970 And so I thought, okay, 1133 00:43:01,970 --> 00:43:04,650 well, all I do is I upload all this content. 1134 00:43:05,570 --> 00:43:08,370 And then I had never used make before, 1135 00:43:08,370 --> 00:43:10,570 I had never even heard of make until you did 1136 00:43:10,570 --> 00:43:13,890 it. Um, and I but I was using, 1137 00:43:14,090 --> 00:43:17,610 um, like ChatGPT to kind of help give me some 1138 00:43:17,610 --> 00:43:18,810 ideas for what to say. 1139 00:43:18,810 --> 00:43:21,130 So I had my own ideas about how to solve 1140 00:43:21,130 --> 00:43:23,010 these problems. But, um, 1141 00:43:23,010 --> 00:43:25,090 I always like to, like, 1142 00:43:25,730 --> 00:43:27,930 send it through ChatGPT or do something 1143 00:43:28,130 --> 00:43:29,930 because, you know, other people have good 1144 00:43:29,930 --> 00:43:31,770 ideas too, right? Not not just me. 1145 00:43:32,410 --> 00:43:36,170 And, um, so I went and I, 1146 00:43:36,450 --> 00:43:38,370 um, I kind of it took me a while, 1147 00:43:38,370 --> 00:43:40,130 but I spent, like, I don't know, a weekend 1148 00:43:40,690 --> 00:43:41,970 putting all this stuff in, 1149 00:43:42,500 --> 00:43:44,220 getting it to work. I did have this one 1150 00:43:44,220 --> 00:43:46,340 glitch which you helped me fix very quickly, 1151 00:43:46,340 --> 00:43:48,380 which I greatly appreciate because just a 1152 00:43:48,380 --> 00:43:50,180 little tiny thing, those are the things that 1153 00:43:50,180 --> 00:43:52,580 do then. And so what I did is I, 1154 00:43:52,700 --> 00:43:54,180 I picked the topic. 1155 00:43:54,180 --> 00:43:56,820 So I run a coworking space for the people 1156 00:43:56,820 --> 00:43:58,500 that I coach. And I said, 1157 00:43:58,620 --> 00:44:00,860 give me questions that you guys have. 1158 00:44:01,340 --> 00:44:03,140 And so what I did is I just put those 1159 00:44:03,140 --> 00:44:04,980 questions in and really short, 1160 00:44:05,020 --> 00:44:06,940 you know, like the one I the holiday one is a 1161 00:44:06,940 --> 00:44:08,260 good example that was came from one of my 1162 00:44:08,260 --> 00:44:10,060 coaching clients. And she said, 1163 00:44:10,100 --> 00:44:11,540 oh, you know, I'm trying to figure out what 1164 00:44:11,540 --> 00:44:13,020 to do about during the holiday. 1165 00:44:13,060 --> 00:44:14,540 Should I stop looking for a job? 1166 00:44:14,580 --> 00:44:16,060 What is I should do? 1167 00:44:16,580 --> 00:44:21,060 Um, and, um, and so I put it in and then 1168 00:44:22,260 --> 00:44:23,820 miracle of miracles, poof! 1169 00:44:23,860 --> 00:44:26,780 All of a sudden, I have something for my 1170 00:44:26,780 --> 00:44:28,940 LinkedIn, uh, newsletter, 1171 00:44:29,540 --> 00:44:31,260 Debbie, where it basically says, okay, here 1172 00:44:31,260 --> 00:44:33,940 it is. Now, it doesn't mean that I don't 1173 00:44:33,940 --> 00:44:35,420 change it because I do. 1174 00:44:35,660 --> 00:44:38,100 Um, but I start out with a very, 1175 00:44:38,100 --> 00:44:40,140 very solid first draft. 1176 00:44:40,550 --> 00:44:41,750 That sounds like me. 1177 00:44:42,270 --> 00:44:44,590 Um, it was able to like, 1178 00:44:44,630 --> 00:44:46,910 for ChatGPT, I was already doing. 1179 00:44:46,950 --> 00:44:49,990 I already had a relatively long prompt that I 1180 00:44:49,990 --> 00:44:52,270 had kind of been modifying here and there. 1181 00:44:52,710 --> 00:44:54,510 And so I was able to, like, 1182 00:44:54,550 --> 00:44:58,070 use that as part of this make process. 1183 00:44:58,310 --> 00:45:02,790 Um, and then I could produce like basically, 1184 00:45:02,830 --> 00:45:04,430 I don't know, like in a morning, 1185 00:45:04,470 --> 00:45:06,110 let's just say, you know, 1186 00:45:06,150 --> 00:45:08,710 maybe for like a month's worth of these Dear 1187 00:45:08,750 --> 00:45:12,310 Debbie columns. And because they're not time 1188 00:45:12,310 --> 00:45:14,390 sensitive, like, my opinion thing is very 1189 00:45:15,030 --> 00:45:17,350 time sensitive, but these are not time 1190 00:45:17,390 --> 00:45:19,950 sensitive. Um, and so as a result, 1191 00:45:19,950 --> 00:45:22,030 I can just produce a whole bunch of them, 1192 00:45:22,590 --> 00:45:24,310 get it off my list of things to do. 1193 00:45:24,350 --> 00:45:26,590 I go and I upload them into LinkedIn so that 1194 00:45:26,590 --> 00:45:27,990 they're just ready to go. 1195 00:45:28,310 --> 00:45:29,830 And then one of my colleagues said, 1196 00:45:29,870 --> 00:45:31,550 well, it's like really amazing, Debbie, how 1197 00:45:31,550 --> 00:45:34,510 you're sending out all this content while 1198 00:45:34,510 --> 00:45:35,950 you're in Southern Africa. 1199 00:45:36,270 --> 00:45:37,830 And I said, yeah, you know, 1200 00:45:37,870 --> 00:45:39,360 well, it's because I did. 1201 00:45:39,360 --> 00:45:41,560 I did all the work before I left. 1202 00:45:42,600 --> 00:45:44,200 Um, now that I'm back, 1203 00:45:44,360 --> 00:45:45,600 I'm still planning to do it, 1204 00:45:45,600 --> 00:45:47,560 like, sometime, probably this weekend. 1205 00:45:47,800 --> 00:45:51,280 Um, I will, uh, generate all the content for 1206 00:45:51,280 --> 00:45:54,440 December. And because I do again for 1207 00:45:54,440 --> 00:45:55,920 newsletters on LinkedIn, 1208 00:45:56,240 --> 00:46:01,320 um, I do need to try to reduce the amount of 1209 00:46:01,320 --> 00:46:03,240 time I spend as possible because of course, 1210 00:46:03,280 --> 00:46:04,840 none of these pay me income. 1211 00:46:04,880 --> 00:46:07,040 Right? But on the bright side, 1212 00:46:07,080 --> 00:46:08,480 they you know, they do. 1213 00:46:08,720 --> 00:46:10,560 They have been successful in bringing me 1214 00:46:10,600 --> 00:46:12,640 coaching clients, right? Because I've been 1215 00:46:12,640 --> 00:46:14,400 doing coaching for years and years, but it's 1216 00:46:14,400 --> 00:46:17,440 only been in the past a year or so that I 1217 00:46:17,480 --> 00:46:20,200 got, um, certified by the International 1218 00:46:20,200 --> 00:46:21,800 Coaching Federation, that I decided to make 1219 00:46:21,800 --> 00:46:25,040 coaching a serious part of my business, 1220 00:46:25,040 --> 00:46:28,040 not just something like I did on a very 1221 00:46:28,080 --> 00:46:30,320 haphazard kind of basis. 1222 00:46:30,400 --> 00:46:32,880 Um, so anyhow, I thank you. 1223 00:46:32,880 --> 00:46:34,200 It really has worked well, 1224 00:46:34,320 --> 00:46:37,400 um, I am hoping now that I'm, 1225 00:46:37,440 --> 00:46:39,920 I was really coming to it very quickly to try 1226 00:46:39,920 --> 00:46:41,720 to get it done before my trip. 1227 00:46:41,720 --> 00:46:44,840 But I'm hoping in December I can advance to, 1228 00:46:44,880 --> 00:46:47,720 you know, doing something that is more voice 1229 00:46:47,720 --> 00:46:50,880 oriented. Because now that like when I wrote 1230 00:46:50,880 --> 00:46:54,120 my book, um, and uh, uh, 1231 00:46:54,560 --> 00:46:56,320 there was not it was just going to be too 1232 00:46:56,320 --> 00:46:58,960 much hassle to do a audiobook. 1233 00:46:58,960 --> 00:47:00,960 But I know I myself only listen to 1234 00:47:01,000 --> 00:47:03,680 audiobooks, so I know it's important. 1235 00:47:03,880 --> 00:47:06,560 Um, so I'm hoping to take again all this 1236 00:47:06,560 --> 00:47:08,480 content now that I have on LinkedIn, 1237 00:47:08,920 --> 00:47:12,320 uh, and, uh, plus my book and then use it to 1238 00:47:12,360 --> 00:47:14,160 do, you know, maybe some, 1239 00:47:14,160 --> 00:47:15,800 you know, a very clear podcast. 1240 00:47:15,800 --> 00:47:17,240 I like the fact that you, 1241 00:47:17,640 --> 00:47:19,480 uh, that was another thing that you clearly 1242 00:47:19,480 --> 00:47:21,600 say, hey, this is like AI Carol. 1243 00:47:21,640 --> 00:47:25,440 Right? So I'm my world is fine with saying, 1244 00:47:25,440 --> 00:47:29,320 this is AI Debbie doing this because I think 1245 00:47:29,320 --> 00:47:31,080 people appreciate the fact that, 1246 00:47:31,120 --> 00:47:33,640 you know, for some of them podcast, 1247 00:47:33,680 --> 00:47:37,810 um, and things that are by sound are their 1248 00:47:37,810 --> 00:47:40,170 best way. They just don't have time to read. 1249 00:47:40,210 --> 00:47:42,130 Write. And for me, like I said, 1250 00:47:42,130 --> 00:47:45,210 I myself, I don't read books as often as I do 1251 00:47:45,250 --> 00:47:46,770 in the evening, like fun books because I 1252 00:47:46,770 --> 00:47:48,770 spent all day reading online. 1253 00:47:49,050 --> 00:47:51,010 And the last thing I want to do sometimes my 1254 00:47:51,010 --> 00:47:52,890 eyes are just too tired to read and I want 1255 00:47:52,890 --> 00:47:54,530 something that's oral. 1256 00:47:54,570 --> 00:47:56,490 Not. Yeah. 1257 00:47:56,850 --> 00:47:58,130 Carol Cox: Yeah, exactly. 1258 00:47:58,130 --> 00:48:00,330 Well, this is why I called it Automate and 1259 00:48:00,330 --> 00:48:02,690 Amplify with AI very much to that point, 1260 00:48:02,690 --> 00:48:05,210 because I wanted to create a repeatable 1261 00:48:05,210 --> 00:48:07,930 system where literally a one click button to 1262 00:48:07,970 --> 00:48:09,410 generate this new, you know, 1263 00:48:09,570 --> 00:48:11,810 on brand, high quality content based on your 1264 00:48:11,810 --> 00:48:13,850 existing content library, 1265 00:48:13,850 --> 00:48:16,730 and then also do an audio version. 1266 00:48:16,730 --> 00:48:18,130 So for those of you listening who have not 1267 00:48:18,170 --> 00:48:20,810 yet checked out my companion podcast called 1268 00:48:20,810 --> 00:48:22,890 Confident Speaker by AI Carol, 1269 00:48:22,890 --> 00:48:24,210 definitely check it out. 1270 00:48:24,210 --> 00:48:26,370 You will find it sounds pretty much exactly 1271 00:48:26,370 --> 00:48:28,090 like I sound right here, 1272 00:48:28,090 --> 00:48:30,050 and it literally at a click of a button, 1273 00:48:30,050 --> 00:48:31,970 and in one minute it produces an entire 1274 00:48:31,970 --> 00:48:34,700 episode. And it's good content because it's 1275 00:48:34,700 --> 00:48:37,060 based on my content library. 1276 00:48:37,060 --> 00:48:39,140 It's not just some generic random content 1277 00:48:39,140 --> 00:48:40,860 from the internet that is pulling together. 1278 00:48:40,900 --> 00:48:42,460 Yeah. So I'm excited for your podcast, 1279 00:48:42,500 --> 00:48:46,220 Debbie, and having more people be able to 1280 00:48:46,260 --> 00:48:48,500 consume your work, your expertise, 1281 00:48:48,500 --> 00:48:50,420 your knowledge, so that they can have a 1282 00:48:50,420 --> 00:48:53,580 greater impact in their careers and on public 1283 00:48:53,580 --> 00:48:55,180 policy as well. 1284 00:48:55,180 --> 00:48:56,580 So for those of you listening, 1285 00:48:56,820 --> 00:48:58,900 look in the show notes. I have links to 1286 00:48:58,980 --> 00:49:00,380 Debbie's LinkedIn profile. 1287 00:49:00,420 --> 00:49:02,540 Definitely connect with her there so you can 1288 00:49:02,540 --> 00:49:04,580 check out her LinkedIn newsletters. 1289 00:49:04,580 --> 00:49:07,420 I have a link to her website as well and 1290 00:49:07,420 --> 00:49:10,260 check out Confidence Speaker, my AI podcast. 1291 00:49:10,260 --> 00:49:12,420 And if you want to check out the AI program 1292 00:49:12,420 --> 00:49:14,020 that we just talked about, link in the show 1293 00:49:14,020 --> 00:49:15,660 notes there as well. 1294 00:49:15,700 --> 00:49:17,580 Debbie, thank you so much for coming on the 1295 00:49:17,580 --> 00:49:18,900 Speaking Your Brand podcast. 1296 00:49:19,260 --> 00:49:20,860 Deborah Stine: Thanks, Carol. It's been great. 1297 00:49:20,900 --> 00:49:21,580 A lot of fun. 1298 00:49:22,140 --> 00:49:23,980 Carol Cox: Until next time. Thanks for listening.