EmoClass stringclasses 10
values | EmoAct float64 1 7 | EmoVal float64 1 7 | EmoDom float64 1 7 | SpkrID int64 1 3.33k | Gender stringclasses 2
values | Split_Set stringclasses 2
values | transcript stringlengths 1 353 ⌀ | emotion2vec_features listlengths 1 1 |
|---|---|---|---|---|---|---|---|---|
N | 4 | 3.2 | 3.4 | 1,408 | Female | Train | experts of all stripes, whether they be coders or journalists, or academics, but we're looking for people who have that deep expertise in some aspect of technology and- | [
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H | 6.4 | 5.6 | 5.4 | 1,408 | Female | Train | ...then, we're looking for people who don't have a lot of policy expertise. so, this is essentially policy 101. i think the current fellows would sort of say that it's- | [
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N | 3.6 | 3.4 | 3.4 | 1,408 | Female | Train | ...yeah. so, we sort of divided into two phases. so, the first phase is really sort of the bootcamp. and then, the second phase, let's call it the incubator phase. so- | [
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N | 1.2 | 2.6 | 1.4 | 1,408 | Female | Train | mm-hmm (affirmative)....in the bootcamp phase and, i'm sort of adjusting the timelines because we actually learned the program was maybe a little too short.hmm.so, we're basically- | [
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N | 3.8 | 3.4 | 3 | 1,408 | Female | Train | ...going with a 10 week program from here on out. so, that's the timeline that will give you. so, if you join the program for this next cohort, the first four weeks- | [
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N | 3.8 | 3.6 | 3.4 | 1,408 | Female | Train | ...weeks, plus an extra day because of holidays, will essentially be a bootcamp. and, there will be three type types of training that you'd get during that bootcamp. so first, there are class- | [
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N | 3.6 | 3.4 | 3.4 | 1,408 | Female | Train | ...es, and that's sort of the schooling portion. it's much less academic and much more practical. so instead of theories of policy, we covered all theories- | [
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N | 3.8 | 3.6 | 3.2 | 1,408 | Female | Train | ...of policy and approximately a 30 minute sort of session. instead, we do things like how do you define a problem? what are seven tips for coming up with a good- | [
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N | 3.4 | 3.4 | 3 | 1,408 | Female | Train | ...policy problem? or, how do you choose between different stakeholders that you want to target? how do you define policy impact? how do you create systemic change? how do you- | [
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N | 3.4 | 3.6 | 3.2 | 1,408 | Female | Train | ...decide between alternatives? and so, each of our classes is really focused on giving people practical advice, and then, having them sort of act out within the classroom setting- | [
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H | 3.2 | 3.4 | 3.8 | 1,408 | Female | Train | ...cutting the process. so, we'll have them research and identify different possible problems that they could solve, or we have them walk through real life examples of- | [
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N | 3.6 | 3.8 | 3.6 | 1,408 | Female | Train | ...developing a stakeholder map. so, it's a much more practical sort of how to do policy than most programs i've seen before. then, the second aspect of- | [
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N | 3.2 | 3.6 | 3.6 | 1,408 | Female | Train | ...the bootcamp is really putting it into practice. so, we have different sessions, in which instead of having the normal academic process where you write a- | [
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A | 6.166667 | 2.166667 | 6.666667 | 1,408 | Female | Train | ...policy memo. and, i would essentially grade it as the quote unquote teacher.mm-hmm (affirmative).we don't want that. we want them to get feedback from me and people involved in our program- | [
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N | 3.4 | 3.6 | 3.8 | 1,408 | Female | Train | ...but we also want those things to go to the real world and for them to get feedback on people who would actually use this. so for instance, we had a policy memo- | [
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H | 3.4 | 3.6 | 3.4 | 1,408 | Female | Train | ...exercise, where every fellow had to write a policy memo. i gave them feedback, and then, we actually delivered feedback. so basically, they were writing on deep things. | [
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N | 3.6 | 3.2 | 3.6 | 1,408 | Female | Train | the commission on trans-atlantic election integrity is actually interested in bringing some of those ideas up to the commission. so, it's deeply possible that the fellow's ideas for- | [
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N | 3.4 | 4 | 3.4 | 1,408 | Female | Train | ...how to manage deep fakes while still encouraging free speech will actually make it to real world stakeholders. and that was a exercise. they did in their first two weeks in the program. | [
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N | 3.2 | 3.4 | 3.8 | 1,408 | Female | Train | then, we also did a 48 hour exercise. so after all the lectures, we basically gave them the opportunity to take 48 hours to solve a real- | [
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H | 3.4 | 3.4 | 3.4 | 1,408 | Female | Train | ...world problem. and in this case, we focused on event security. so, how do you improve physical security for big events occurring in cities?mm-hmm (affirmative).so, we got rep- | [
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H | 3.8 | 3.8 | 3.6 | 1,408 | Female | Train | ...those ideas. the city has already taken back to try to have real world impact.cool.so, that second portion is really about giving them an- | [
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H | 3.8 | 3.4 | 3.4 | 1,408 | Female | Train | ...experience, not just of doing policy, but doing it in a way that can have impact from the very first day they walk in the program. and then, the third aspect of the- | [
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N | 3.8 | 4 | 3.6 | 1,408 | Female | Train | ... bootcamp is really the substance. so, we're focused on tech policy. so, we bring in experts from different aspects of tech policy to give- | [
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H | 3.8 | 3.6 | 3.6 | 1,408 | Female | Train | ...people sort of the state of the art and to inspire the fellows as to what topics they might want to work on. so, we had chris riley from mozilla come in- | [
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N | 3.2 | 3.4 | 4 | 1,408 | Female | Train | ...and to sort of educate the fellows on that. on tech policy 101, we had tara lyons from the partnership on ai, come in on ai 101. | [
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N | 3.6 | 3.6 | 3.6 | 1,408 | Female | Train | we had beth george, who used to run dod sort of in the legal office. she came in to talk about cybersecurity 101. so, we really- | [
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N | 3.4 | 3.6 | 3.8 | 1,408 | Female | Train | ...got sort of high level people to come in and teach the fellow about the topic. and then, we brought in a panel of experts from different perspectives to say, "here's what i wish technologists- | [
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N | 3.6 | 4 | 3.4 | 1,408 | Female | Train | ...were buzzing were working on in this space. and, in addition to that, of course, we've had a bunch of series of people come in. we had a dinner with janet napole- | [
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N | 4 | 3.8 | 4 | 1,408 | Female | Train | ... former secretary of dhs last night. we've had we're reporters. we've had people representing cities. we've had all sorts of experts. we met with the lieutenant governor of the state of- | [
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H | 3.6 | 3.4 | 3 | 1,408 | Female | Train | ...california. so, we've really tried to expose them to people in the policy space, and especially in the tech policy space who could inspire the fellows. so, that's sort of the- | [
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N | 4 | 3.2 | 3.4 | 1,408 | Female | Train | ...boot camp side of things. and then, at the end into the 48 hours, the fellows then sort of transition to phase two, which is working on their final- | [
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H | 3.2 | 3.2 | 3.8 | 1,408 | Female | Train | ...projects. so, to get into the program, you have to pitch an idea of what you might want to work on. and so, we get a- | [
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N | 3.4 | 3.4 | 3.8 | 1,408 | Female | Train | ...using technology for good or social justice. so, those are some of the areas that we look for, but then, the fellows sort of bond as a class, and this class is- | [
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N | 3.2 | 3.8 | 3.6 | 1,408 | Female | Train | ... super well integrated. they really spend a lot of time together, and many of them wanted to work on projects together. and so, they sort of did a brainstorming session- | [
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N | 3.2 | 3 | 4 | 1,408 | Female | Train | ...towards the end of the bootcamp, in which they decided on final projects. and then going forward, you would essentially have six weeks, a design sprint, to scope your problem- | [
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X | 3.833333 | 3.833333 | 3.5 | 1,408 | Female | Train | ... to do research, to come up with a stakeholder map and to develop a really practical output of some kind that could help the stakeholders. so- | [
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H | 3.6 | 4 | 3.6 | 1,408 | Female | Train | ... it could be an app or a website. it could be mock legislation. it could be an operational plan for a new program. it could be a game. | [
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N | 3.4 | 3.8 | 4 | 1,408 | Female | Train | these are all outputs that our current fellowship class are considering. but what we really want in that second phase is to produce real world outputs that have an impact on the stakeholders. | [
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N | 3.6 | 3.4 | 3.8 | 1,408 | Female | Train | sure, so it's a real interesting diversity. so i'll give you three examples off the top of my head. so first, we have a fellow who comes to us | [
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H | 6 | 5.4 | 5.8 | 1,408 | Female | Train | all sorts of different things. and so this fellow identified early on that policy is not made that way, generally. we don't normally test | [
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H | 3.6 | 3.4 | 4 | 1,408 | Female | Train | their ideas. we tend to sit in a room, come up with an idea, write a paper about it. and then if you're lucky, get it to a stakeholder who will implement it in that's | [
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H | 3.8 | 3.6 | 3.4 | 1,408 | Female | Train | a lot of excitement around that type of idea, and i think it's essential. and i love the fact that that project is really trying to break the | [
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H | 3.2 | 3.6 | 3.4 | 1,408 | Female | Train | policy process as a whole. it's trying to hit systemic change, not by take one particular idea, but by giving policy makers a new tool coming from computers | [
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H | 3.6 | 3.4 | 3.4 | 1,408 | Female | Train | right. and i think coming up, it's been an interesting translational exercise too, because, for the fellow, it is so obvious how | [
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H | 3.6 | 3.6 | 3.4 | 1,408 | Female | Train | because i think that that's one of the challenges. but it's been great though, as i'm sure all your listeners are aware, there's really a handful of people | [
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H | 3.6 | 3.4 | 3.4 | 1,408 | Female | Train | who purely cross that technology and policy divide, bruce and i are being a great example of them. and there's been a lot of excitement from that. | [
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A | 5.8 | 1.8 | 6.6 | 1,408 | Female | Train | yes. so a second project is really focused and i think, unsurprisingly, since we have a few academic, there's a lot of interest in working with | [
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U | 6.6 | 5.4 | 5.4 | 1,408 | Female | Train | companies are getting sued.as a lawyer, i very much appreciate this problem, but there's lawsuits, there's the gdpr, the european privacy rules, | [
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N | 3.6 | 3.6 | 3.6 | 1,408 | Female | Train | one of our fellows are particularly concerned with instances, for instance, where human rights abuses may appear briefly online and then are taken down because | [
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H | 3.6 | 3.4 | 3.2 | 1,408 | Female | Train | rights of these investigators are not able to actually access that information. and at berkeley, there's a great center, the human rights center, that's focus specifically on that problem. so | [
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N | 4 | 3.2 | 3.6 | 1,408 | Female | Train | solutions by which you could actually have better sharing.so is there a way to sort of sandbox the information or provide some sort of | [
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H | 3.6 | 3.2 | 3.6 | 1,408 | Female | Train | facing the same problem and have the same opportunity for engagement? so i think that that's another project where we're sort of combining in this case, the | [
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H | 3.2 | 3.8 | 3.2 | 1,408 | Female | Train | we're really hoping to see what sorts of stakeholders need to be in the room in order to produce solutions in that space. so | [
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H | 3.8 | 3.2 | 3.2 | 1,408 | Female | Train | and then i guess the third project, there are a couple others that could share. there's so many that are exciting.sure.but this one is already, i think, making some good headway | [
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H | 3.6 | 4 | 3.6 | 1,408 | Female | Train | so it's a good example of the sort of rapid response time. one of the things we tell the fellows from day one is that policy does not work at the speed of | [
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N | 1.6 | 2.6 | 1.4 | 1,408 | Female | Train | days, eight days, two months to produce something on a timeline, you'll get a call at 9:00 am. and by noon you have to have | [
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A | 6.4 | 1.2 | 6.6 | 1,408 | Female | Train | the language in front of the person that you want to impact, or you've lost your opportunity. and i think that came as a surprise to many, especially of our coders and startup founders | [
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H | 3.2 | 3.6 | 3.2 | 1,408 | Female | Train | whose timelines are fast, but they're not fast to that degree.right.and so one of our fellows is working on a project focused on the state of florida | [
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N | 3.4 | 3.2 | 3.6 | 1,408 | Female | Train | issues we were just discussing about human rights abuses online. the solution the state of florida has created is to collect tons of data on | [
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H | 5.6 | 5.4 | 6.2 | 1,408 | Female | Train | all students without an opt out clause that will therefore enable this huge data collection. and they've asked for that to be put together in a | [
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H | 3.4 | 3.2 | 3.2 | 1,408 | Female | Train | very short amount of time. i don't remember the exact length of time, but a matter of months. some firm is going to be contracted to collect data on many students | [
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H | 3.4 | 3.6 | 3.6 | 1,408 | Female | Train | and so our fellow has rightly identified that technologists are not being consulted in the process of building that database, in the process of how it should run in the | [
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N | 1 | 3 | 1.4 | 1,408 | Female | Train | security and privacy and other types of effects that may happen as the result of that.and so this fellow sort of realized that there was a huge | [
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N | 3.6 | 3.4 | 3.4 | 1,408 | Female | Train | set of problems and her background is really in educational technology. so she's trying to create a bunch of different types of outputs from games to advertising | [
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H | 3.4 | 3.2 | 3.8 | 1,408 | Female | Train | campaigns, to really try to get stakeholders into the space to be aware of this new program coming down the pipeline and to give them tools to actually enable them | [
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X | 3.8 | 3.6 | 3.2 | 1,408 | Female | Train | to advocate on their own behalf for how this new technology should be shaped. and so she's plotting along | [
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N | 3.6 | 3.8 | 3.8 | 1,408 | Female | Train | on her four week timeline and then reaches out to a stakeholder who's like, "oh, we should definitely try to place an op-ed on this issue." and so our fellow is quickly | [
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H | 5.6 | 5.8 | 6.2 | 1,408 | Female | Train | learning how to write an op-ed, writing an op-ed, and we're working to place that right now because having the voice of a parent from florida | [
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N | 3.4 | 3.6 | 3.6 | 1,408 | Female | Train | who is also a technologist is a unique voice in that space.and so it's been really great to see as our fellows start to engage, | [
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N | 3.4 | 3.6 | 3.2 | 1,408 | Female | Train | even in the early days of their projects, how sometimes we have these immediate reactions that enable them to have real world impact right away on things that they really care about. | [
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N | 1.2 | 4 | 1.8 | 1,408 | Female | Train | so we're really proud to see that this is far from an academic exercise. there are real problems and way more than we can solved | [
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N | 1.6 | 3 | 1.4 | 1,408 | Female | Train | all. it's just a small amount of fellows, but there's tremendous appetite for technologists who want to engage in solving real world problems. and so that's been really hard. | [
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H | 4 | 3.6 | 3.2 | 1,408 | Female | Train | so, i mean, i think a couple things, so first the basic requirement is you have to be 21 to apply for the program. there's no specific academic requirements | [
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N | 3.2 | 3.2 | 3.8 | 1,408 | Female | Train | we love all types of creative people.you do need to be able to show though that you're a technology expert in some way. so regardless of your | [
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H | 3.8 | 3.8 | 3.6 | 1,408 | Female | Train | background, we want to know what is your expertise in with regard to technology and how do you want to bring that to bear in a program like this. second, we're | [
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N | 3.6 | 3.4 | 3.4 | 1,408 | Female | Train | looking for people who are really passionate about solving real world problems. as i mentioned, this is not one of those fellowship programs where you just sort of roll in, sit around for 10 weeks | [
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U | 5.333333 | 4.333333 | 5.666667 | 1,408 | Female | Train | meet some cool people, go to a few seminars and work 20 hours a week. it's a real intensive program in which we teach | [
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N | 3.6 | 3.4 | 3.6 | 1,408 | Female | Train | to a lot. we hope we give you a lot. we certainly introduce you to some tremendously amazing people, but you really have to have the energy to want to participate | [
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N | 3.6 | 3.6 | 3.2 | 1,408 | Female | Train | in the program.it is a full-time program. about half of our fellows are on leaves of absences from their jobs to participate. and about half are | [
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X | 6 | 5.166667 | 5.5 | 1,408 | Female | Train | sort of in transition. their startup was wrapping up or they were able to sort of take some transitioning time. so that's usually the profile of people we | [
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X | 3.2 | 3.6 | 4 | 1,408 | Female | Train | look at but it is a full time program. we ask that people be in the office four days a week, and that they're working five days a week for the length of the fellowship.and | [
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N | 3.2 | 3 | 3.6 | 1,408 | Female | Train | the office is where?so right now we're actually sharing with code for america. so we're at fifth admission. we haven't confirmed | [
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N | 3.2 | 3.6 | 3.6 | 1,408 | Female | Train | ... formed a space for the next cohort yet, but it will be in either san francisco or oakland, highly likely in that powell 00:00:00:19] or montgomery street- | [
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H | 3.8 | 3.4 | 3.4 | 1,408 | Female | Train | ... [bart 00:00:24] area, because we want to make it possible for people from the peninsula, people from oakland-right.... people from wherever in the bay to come. and by the way, you need not be local to the bay. we do pay reloca- | [
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N | 3.6 | 3.6 | 3.2 | 1,408 | Female | Train | ... tion. about half of our fellows came specifically to spend time in the bay. so if you are somebody who is located somewhere cold and want to- | [
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H | 6.2 | 5.2 | 6.6 | 1,408 | Female | Train | ... come spend january and february in the bay area, that's great as long as you're passionate about also doing the program. we are looking for people- | [
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H | 3.4 | 3.4 | 3.4 | 1,408 | Female | Train | ... who can write clearly.mm-hmm (affirmative).no matter what type of policy you're doing, you need to be able to write. and so we're looking for people able to really show that off. and so we- | [
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H | 3.6 | 3.4 | 3.6 | 1,408 | Female | Train | ... do ask that people write a sample policy memo. we have a couple webinars coming up on the 7th and on the 14th that will train people on how to engage- | [
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... |
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