Beyond Boundaries 2018: Lightning Talks

Beyond Boundaries 2018: Lightning Talks

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All. Right thank you everybody for coming but, we have anybody who's still in the memorabilia. Room the exhibition, hall we encourage you to come into the lecture hall here and join, us as, we kick off the third annual symposium. On, hybrid, scholarship. At Yale University. Beyond, Boundaries, and this. Event is one. That is organized, every, year by. A team led by Cathy, D Rose who's, the manager, of the digital humanities lab this event for, the last three years really, has been the work of Cathy and it's, been supported by a great team of others, inside, and outside the lab we're. Especially happy to work alongside Yale. Steam, which, is a student-led, group here, at Yale University, steam. As most of you know stands, for stem. Plus, Arts and Humanities with. A silent age we're, really excited to be working with them and they've helped us in putting together the program for today's events. So. The exciting, thing about Beyond, Boundaries of course is that it brings together folks, from all parts, of the scholarly, spectrum, at Yale University and, today you're. Gonna hear talks in roundtable. Discussions, for, folks who work in our robust, museums, galleries, and libraries organizations, you're going to hear from undergraduates, graduate, students and faculty it's. Really exciting to have all these parts, of what makes Yale University, great here today, when. Cathy asked me to welcome you all to the hall I asked her what are the things I need to do because you've organized everything though you've managed, to get the room renovated, all this great program really excited, what, out what could I possibly do, that would be useful and she said two things the. First thing to do is to make sure that everybody's power point works so I'm gonna be up here during the as we switch between different keynote presentations. Different lightning talks to make sure that everybody's, power point is working well and she, said the other thing you need to do is to keep us on track so, for all of our wonderful lightning, talk presenters, I'm gonna have little one minute warnings, and then this, is the cane that Yanks you off the stage just. So we can get through everybody's, work.

And So. With that I'd like to kick off our lightning talks which are going to be presentations, by students, and postdoctoral, Associates on subjects. Related to digital humanities, and steam, projects, here at Yale and the. First of our lightning talks is going to be science. Stories, using, triple if' and wiki, data to, create a linked data web application, and, this is going to be a presentation, by Kenneth seals nut and Kat, Thornton so Ken and Kat. Hello. Everyone thank you for allowing. Me to speak at this opportunity, my. Name is Kenneth seals not I'm a senior computer science major, and. I'm working in concurrent, with Kat Thornton who is a CLI. Our fellow of, computer. Science as well as in the digital preservation department, and the library. And. What, we have been, working on this this, past year is called, science stories and. What that is is a, application. That we're gonna leverage two resources, that are. Widely used by other projects, here at Yale want the first being wiki data and the, second being the triple ayah framework, and I'll touch, a little bit on what. Each, one is as well as give a brief, demo of the. Application what, we've built so far, so. First of all I'd like to start by just explaining the team again Kath thorn Thornton. Is a, CLI, our fellow of computer science I'm, a senior. Undergrad. Student here and. This, also doubles as my senior project for the computer science department and professor. Benedict Brown is our, faculty, advisor for this and. So. I'll, first give the like high-level goals of the project and then break, it down into. What, things are being used within. The project so our first goal is making. A visual storytelling. Application. Of. Academic. Research one thing that we found, with. Today's. Great. Resources, of computing, as well as a, lot of frameworks that have been built for on the web is. That we can renovate, how we go, up explaining, our scientific, and academic research, to the public. One. Thing that we found is we. All do great research here, in these institutions, but, if someone, that doesn't have access to yells, catalog or another. Catalog of interest it's really difficult and cumbersome to find out this information so. Our, first goal was using images, videos, multimedia. And incorporating. That with, with. Our academic research to paint.

The Picture of who we're actually researching, and provide. More contextual. Information there, and then. We're. Also going to expand this out using the Wikimedia to. Crowdsource some of the content one. Thing. That is. Really. Beneficial, that, of you. Know a lot of research projects, is these, stories can be told and a lot, of people don't have to do. All of the busy, work of or. All of the overhead work of finding out all of this information that other people have already researched so, if we can find a way to leverage more, crowdsource content, incorporate. That with scholarly. Academic. Research. We'll be able to tell, more about the person and I'll. Also demo, the how, triple-i of connects a. Lot. Of research institutions, together through, this it allows for. Authorship. As well as annotations and citations, to be, transferred. From institution. To institution, in. In, a very safe way that, allows. A lot of interactive, interactivity. For the users as well, and. So ultimately. The goal is to bridge, the gap between information. That's currently in print digitizing. That and, putting. It on the web and making it accessible and, the. Project as a whole the, main. Reason why we decided to go about this is. To celebrate 150. Years of women at Yale which, is coming up next year and. So. What. We wanted to do is, cat. Has been, doing. Research on the. Early women at Yale for the past two years and, she. Approached me with the, research. That she's been doing and I thought it was completely fascinating. And we both came to the, to the consensus that more people should be able to know about this information and not. Just be able to read a paper about it but also be able to interact with it, so. We're. Starting this project on these first five women because they. Are. They. Have resources in a lot of different areas only on Yale's campus and, so, we're, starting here to show that we can leverage all of Gail's. Research. Across, the library, as well as the Peabody Museum as, well as the department, and the, faculty, to all tell, have, a part in telling the story not, just a library, or not just a museum on campus, and. So. First I'll. Explain a little bit about what, wiki data is for, those of you who do, not know it's a. Lot of people are familiar with Wikipedia the English Wikipedia, wiki. Data is. Another. One of the projects, that the Wikimedia Foundation. Has. Adopted and what. It is is, it's. Machine readable open link data and. That's. A little long, phrase but essentially. It allows people, to create these statements. So. For, example this is Grace, hoppers. Item. On wiki data right now and. So it's very clear statements, of what. The. Like bare-bones. Data. Is about this person, and it goes on. Pretty. Pretty, lengthy but it just gives you an idea so for example like instance of human. Image and then it has the link to it sex, gender female, so, now. We can feed that information to, a computer and. It's all unique. Identified. With. These identifiers, z' here, and. So, that allows not. Only humans to be able to read, and understand this information but also using algorithms to curate this data as, well as to, know, that you've hit exactly, what you're, looking for in terms of research and. Then. We're coupling, this with using triple I F which. Stands, for the International image in our interoperability, framework. And, what, that does is that it's. A standard, set of image, api's. That. A lot of research institutions, came together to. Determine. This, is how we're going to, transfer.

The Metadata about our pictures when. We're sharing these images so instead. Of having people just copy and paste or download images off the web now you, can export, a JSON, package, that has. All of the metadata so that includes the licensing, the. What's. Actually a descriptions, of what actually in the images and outside of just a caption. Links. To where this resources, and if, for. Example if it's digitized, it we can link back to what, the actual collection is in the in the archive that it's being referenced. To and so just I like this picture on this on on, the side here because it shows how we can make this practical. For example. This. Is a display in Boston College right now where as. You. Know you, can't really have. The public handle really, old manuscripts, and and text. So this would give you the possibility, to digitize, that information, and have users flip, through the book page. By page in a much more interactive impersonal. Manner than. Then. Just by staring at whatever page that's. There and. So. I will quickly, just explain that what, our application does, is it's going to, give. You the ability to. Combine. Both, the, information. On the Wikimedia Foundation. YouTube, videos, as. Well as books and. Any other content that we can find all into one place and. I'll. Just do a brief demo of. What. That looks like right now this is in a prototype phase. And. So it takes you to this landing page, and. Right, off the back it shows the, first five women that we're, planning. To do research one and, when. You click on them it shows you a little, description about who there is and it's pulling this information from Wikipedia and, then you can view their story. Here. And we decided to go the slides method, so you click, through and these. Are all not just for one person this is just show the extent of how. The how. The application. Is going to work so this is all not for one person but for, example this, is a triple I up image viewer right here and you can hover over who's. In the image, understand, more about the content and, click. On it and and. As well as have all of the information on how to cite, the source and give credits and attributions, and. Then, on. The very last slide it'll. Show you all of the current wiki data information about them. And so, yeah that's where. We're headed, what. We've built so far and. We're. Planning to present this the, first official demo of our first five science stories at the women's faculty forum in May as. Well as we'll be presenting the full prototype, at the. Triple I of conference, um in, DC and we're, submitting a paper tonight actually for presenting. This to the international semantic, web conference, in October, so, thank you guys very much for your time. Let's. Just jump ahead to Amy Juliana, who's going to be speaking on abroad, but now accessible via, our educational, tours. My. Name is Amy Giuliano, I study. Religion. In the visual arts here at Yale University and. I wanted to begin by. Inviting you, to. Imagine, that. You're standing at the threshold of. St. Peter's Basilica in, Rome. You. See bronze. Doors, monumental. Bronze doors towering, at five times your height above you and a. Guide beckons, you into a wide nave pierced.

With Shafts of light and. Immediately. Directs, your attention to the right where. You see, Michelangelo's. Pietà as. You. Walk, around this masterpiece, and. Consider. Its various angles, the, guide describes to you the genius of Michelangelo, and of, Renaissance humanism. So. What I just described, to you is the. Dream of one of my students. Let's. See. Her. Name's Alex, Alex. Longs, to study abroad in Rome and especially wants to study some of the art and architecture of the city however, costs, an opportunity, prevent her from doing so and Alex. Is not alone in fact. Less. Than two percent of us, college students study, abroad annually. The. Obstacles, for most are cost. Opportunity, and, access. So. I. Lived. In Italy for several, years I'm an Italian citizen I, worked for years as an, art, historical guide in Italy specifically, in Rome but some of the other other major cities I. Currently. Teach at, Sacred, Heart University in, Fairfield, Connecticut as, an adjunct professor and. My. Frustration, was. That I really wanted to share with my students, the transformative. Learning experience. Of being, within the living classroom. That certain incredible, cities provide. So. I felt. Frustrated. That my students were so many of my students were missing. This transformative, learning experience, so I decided, to, tackle the problem head-on and, I'll. Present to you my. Proposed. Solution, so. Two, weeks ago I went to Rome with. Some kind, of high-tech photography. Equipment and created. 3d, virtual reality tours. Of some of the major sites of artistic. Historic. And cultural. Significance, around, the city I. Created. 3d, use. Using, 3d high resolution, photography, laser. Scanning. 360. Photography, i virtually. Reconstructed. Some of the interiors, of these sites, and. Made. A virtual, tour so. I'm, coupling. This, immersive visual. Experience, with an interactive virtual, tour, so. Students. Feel as though they're walking through a 3d, space and, they, can stop, click. And explore, points, of interest, that. Are linked to audio-visual. And written content. So. Here, are some of the sites that I covered while, I was in Rome. Major. Sites of art mostly, are historical, significance but, also historic. And cultural significance as, well and I. Chose. To to, share with you here. We have a. Classic. For any art history course the mausoleum, of Santa Costanza. Is the 4th century, building. And. I'm. Just gonna go through I won't give you the tour because we don't have time but I. Can explain to you some of the advantages, of using, this, type of technology as you can see the, student moves around the, space and can, look, all. The way around. Oops. I, can. Get the mouse work there we go. There's. These, buttons here that allow you to zoom in for high resolution detail. You. Can zoom to different areas, there's. A highlight reel. You. Can kind of see how it works here so. This, church took about 2, hours to. Complete. This is a replica of the. Sepulcher. Of Costanza. Which is now in the Vatican Museums. Sorry. The mouse is a little difficult on here but you can kind of see what I mean, here we have. Santa. Maria sopra minerva. Which. Is a huge church, it's. A basilica, in rome it. Has works by Michelangelo, Philippe filippino lippi the. Grave of Catherine of, Siena, gadget, n' and Frangelico. Here's. A dollhouse view, and a floor plan you can. See that just as an example because I'm currently working on creating. The interactive, tours with audio visual. And textual. Elements. Added, that I put a little tag here and so. The student can go into that Chapel. Look. At these, 14th. Century. Excuse. Me 15th century frescoes 1493. I. Brought. My students, in here specifically, because we were reading Summa, contra Gentiles, by Thomas, Aquinas and, here he is and. It's an illustration, of that work so, they explore these frescoes you can zoom in for.

Very. High detail if. The computer doesn't crash. Here. We are, and. Again, you can see the tag that I put in. Is. Just an example. There. We go you, click and read all about this this, space. These. Little. Areas. On the floor indicate, places where you can zoom. In and out and, kind. Of the next step in. The tour. I'll. Bring you to the altar and then we'll leave, this space. Here's. Catherine of Siena's body underneath, the altar. And again, I'm setting, this up so that students, can see this in virtual. Reality headsets, so. The. Experience, will be really, three-dimensional. And. If you turn around here whoops. This. Is Christ, the Redeemer by, Michelangelo. So. I, at. Walking, you through this I'm trying to give a sense of. The. Advantages. Of using this sort. Of tool. Pedagogical. Tool we. Jump back to my presentation. These. Are my students. Exploring. The Minerva as they read Summa contra Gentiles. And, again. Some of the advantages, include, this is an immersive unforgettable. Experience, it's interactive. It's, engaging. It, does, what video cannot you're not passively, going. Through a space you're really interacting, with it you're exploring. Its. High high, resolution, you can explore. Sculptures. At different angles you can consider their scale you consider the, context, of yourself within this enormous, space and. I'm, my. Little plug to you is please, help me give access to students, to sites that they may otherwise never. Experience. And. Here. Are, some. Of my students, I'm. Pleased, just to conclude I'm pleased. To. Announce, that I've. Received a fellowship with, SCI, city to, work. On this project for two months over the summer and kind of get it off the ground, make some more tours thank. You. Thanks. So much to Amy and now we'll move on to the media concept, which is going to be presented, we've worked from Anna Schekman and Zachary, KITT. Thanks. So much to Peter and, Kathy and Doug and everyone at the th. Lab, unfortunately. My, my, partner. Zach. KITT, who is. At. The Jackson Institute, at Yale can't be here he was a programmer, before. Coming. To Yale, and he's. Actually. Just about to graduate and be a programmer again. And. He. Has been working with me I'm a PhD. Candidate in, film and Media Studies and. I, am, working on a.

Dissertation. That, is called the media concept, that's. About the evolution, of the term media in the second half of the 20th century specifically, about when when, media entered the vernacular, and. The. Ramifications, that I had for different culture industries. So. It's a very literal project, it's very word based and so I, actually. Was inspired to work. On it and think about it because I was fooling around on Engram, you are which many, of you may know just Google. Google. Interface. Here you'll. See that around you. Know after World War 2 there's, the sky rocket in the in the in the. Occurrences. Of the word media in all the text that well. All the text up to 2008, that, Google. Books has and, so, I was, interested in that phenomenon, and also. Interested in some of the different. Iterations. Or. Offshoots. Of this of the media concept and their and. And, their evolutions, over time and. So I wandered, into the d-h lab and ask them about Ngram, viewer and, also if there was a way to have more control over it because, it, it, sort of just. Calls from. This massive amount of text, that is that, is Google Books, and. If. You actually go in and you sort of it allows you to sort of click around and see, which. Books in particular it's coming from and sometimes you'll see that actually the metadata is wrong and a book that it says is from. From. 1940, as an in fact is in fact a book from 1812. That was republished, in 1940, and you know it's just not very precise and. The fact that it goes up to 2008 is also not ideal because. It doesn't go up to the present and that is that is for all, sorts of reasons involving, copyright, and law, so. I, wanted, more control over over. Over. Ngram. Viewer and, Google Books and so I wanted into the gh lab and asked them if I could get that and. They said that I would have to build it essentially, and. So I. Learned. About a corpus I learned about corpora about building your own, basically. Scraping, text from all aspects, of the web or if, it's not digitized, digitizing. It and trying to create your own. Your. Own, database. Essentially, that then you can manipulate with, various, different tools all of which I'm still you know my learning curve I will admit it's still very steep and I'm very, grateful to the, patience of both sach my assistant, and, and. Everyone. At the th lab so I'll tell you a little bit about what Zak and I have done. We. Started, with the Vogue corpus which, is. Which. Is available through the D H lab thankfully, Yale has that and, so we, have. We, have, all of these PDFs of articles from Vogue in the twenty from the twentieth century and we, actually, ran, what, is which, a Google, algorithm called word to vet, word. Embedding models are as, far as I can tell. The. Most interesting, aspect, of of. The. Textual digital, humanities, they're just super exciting, because they allow. They. Allow, those. Who have any facilities with with. With, the algorithm to track the semantic and syntactic elationship.

Sub Words over time how. They do that is fairly complicated and I can, definitely, talk to you afterwards, and also would absolutely, point you to the blog, posts of Ryan Hughes er from Stanford, and Ben, Schmidt from Northeastern, who are really. Facile. And agile at, writing. In between, in. In. Between the sort of lexicons of the humanities, and, the Computer Sciences but, basically, they take, all. Of the, words in your corpus, and. They. They, plot them, visually. In a in a in, a, space of n dimensions, multi-dimensional. Space, and they, the. Theory. Behind this is you can actually get related, words from. One query so I can query media and find all the related words and the principle is that these. Semantic. And syntactic evolutions. Will be tracked by the company, that a word keeps over time and. So here let's, we just a query media in this VOC corpus, you'll, see one of the most amazing interesting things for me. To. Find is that you, know in in the in, the 40s and 50s you, you get a lot of. Let's. Say a. Not. Very abstract, pretty concrete nouns. Manuscripts. Criticisms. Pallets all so they're they're all narrow plural, and. Then. As you move through time you start to get adjectives. And. Also, that, would that would adjectives, that might qualify a singular, like the consolidation, of media as a singular concept, cultural. Historical racial political religious to me that signals, I mean this is not this, is a small, data, set still vogue right. One. You know the board medium might not show up as much in vogue as it might end a the New York Times but where we're moving in that direction, Zack, and I. So. To me that signals the consolidation, of a concept because, you have it's being qualified all, of a sudden as opposed to being substituted. And, metaphorically. Or metonymically. By. Other, other. Nouns. So. Then. Here. Is where things get really kind, of, psychedelic. And interesting. So. The, what. Word Tyvek allows you to play with is, arithmetic. But. Linguistic. Arithmetic, so, you're, able. For example to, take to go into the corpus and give it give. It a. Give. It a an equation so let's say you wanted, to see, to see how how, sort of, how. Rigorous. And extensive your, your corpus was if it was really good, and again this algorithm gets more precise than more text, there is so the bigger your corpus is you. Could you could you could theoretically put, in this query King + man - woman and you would most likely get Queen, because, it's that smart. Which is a little menacing but also really interesting. So. In the in the vogue corpus just for an example if we put in this at this algorithm. Or this equation. You'll. See that in nineteen forty fifty sixty all. Of, the first the most related words were in fact Queen then. Interestingly in, nineteen. Seventy I'm not sure why it's hairy and the third most the most. Most. Related. Word is queen but I also thought it was kind, of Awesome than in the vogue corpus, the, most. Related word to, that, should be queen is actually Thatcher in the nineteen eighties just. A glitch, of history, um, so. Then. Zack. And I were just sort of playing around with more queries, like this with we, have a big, big list of them and they're. Not I'm. Still not quite sure what to do with them but this interesting thing that you can do essentially is if, you say medium, - medium and you you, throw that into the computer, you'll. You'll hopefully be able to approximate, something. Like. It. Will, take away let's say all. Of the words that are that are, both. The most related to media and both that really most related to medium and you'll try to get to a different type aspect, element of the concept. To. Me I was thinking about when I was chose these particular wants to show you media, - media media, + arts media - press I was trying to sort of get at these different aspects of the media concept, its, relation, to the concept of media more platform. Its. Relationship, to. The. Phenomenon, of mass culture its relationship, to. To. The the, proper that, or to the article uh right so so, media - press. I was sort of trying to get at what, if you take away that the sort of very activity of the press is there's some way to get this sort of environmental, or. Atmospheric. Quality, of media that's not actually professional, but is that but it's more sort of experiential, I mean these are just. Aspirational. Aspirational. Algorithms. Here but we've got some really interesting weird stuff like medium - medium, the third most related word is. Its. Pudgy, Vic who is was a a, was. The chairman of Conde Nast 4 from much of the 20th century so that's interesting and weird again, this is just the vogue corpus, and so where exactly I, are going, with this yeah, this I thought was interesting that media. Plus arts over time you start to get these sort of what. We would think of as mediums. So. Zach and I started working on this with the, Atlantic, and I'm, actually now working with Richard. So at McGill.

Who Has access, to the Hadi trust. Databases. And we're actually just sort of trying, to put together a huge magazine, corpus, in the 20th century and run word to Beck on, that but, you'll, see that the Atlantic one for a variety of inter ayat, e of super interesting reasons just, didn't quite work in. In. That. It's. Not big enough we were. Only able to just, because of time and resources. Zack's, and mine not the DHS, okay, we, were. Not actually, able to get all of the text from the Atlantic but just the words that just the text that had the word media in them and that just wasn't enough text for the word Tyvek algorithm, to be effective so you get these really and the way we know that essentially. First. Of all the way you know that is it before, 1960, there's just not even enough text, there at all to even make to, have data. Also. None. Of these are Queens. Sometimes. Country, maintain forage Bishop there's just it just didn't it just didn't quite work so, hopefully. As we continue. This project and we get a bigger and bigger corpus and two people actually we're. Hoping to actually be able to approximate the. Way. That the media talked, about media in the 20th century so, is. Thanks. So much and, now, I think having. Failed. Once at being able to get. The PowerPoint working, correctly I think we now can invite up jasker, Feinberg, to talk about co education, at Yale through the eyes of the pioneer that, managed to get it working Thank You jasmine. So. First of all thank you all for being here obviously, I'm. A sophomore, undergraduate. At Yale studying, mechanical engineering, and actually did, this project as part of a digital humanities class at. Yale, which is a seminar, offered to all undergraduates I believe some of the teachers of that actual class are in the audience but I won't embarrass them, by pointing at them specifically. So. The goal of this project was, to, show. How, Yale became co-educational, but. Our true goal was to show how the, use of digital multimedia, could, really bring that process, to life and given the time frame I want to dive, right into that so, our goal was to not, just sort of give it a, textbook. History, of Yale's process. The co-education but, to really sort of make our audience feel, a lot of the actual struggles. Discrimination. And image, the context, of this era and we. Chose to do that by focusing on one person, in particular as, opposed to showing a broader, spectrum that. Person happened to be Margaret Homans who is currently, an English professor here and was. Member, of the second. Class of women at Yale, but. Before giving a full, rundown. Of her story, I assume who sort of established, the context, of co-education. At, Yale so as, some of you may or may not know yield actually did not become co-educational. Until 1969. And. It. Sort, of became co-educational as, the women's movement and broader, movements, at Yale itself, began to strongly push for it but, it should be noted that Yale, was not exactly a pioneer. Despite. The title of this project, in the field of co-education. By. 1897. 56. Percent of all, institutions were, already co-educational.

By. The time that Yale actually became, co-educational that. Number was in the 80 percents so. To give sort of a map. This. Is Yale obviously. The. First school to become co-educational. Is Oberlin, in 1837. Once. Oberlin became co-educational then. About. Nine more universities, became co-educational in, the 1850s. Including, Baylor, Lawrence. Antioch, but the movement still not started it was mostly small colleges, and the. True shift, towards co education had not begun but. That became, much, more pronounced with the beginning. Of the land-grant universities, especially in the western region which, if you don't know our large public, institutions. That usually. Began, co-educational. And that really drove the push. HBCUs. As well began, become co-educational. In the late 1800s. Now. If we fast forward a little bit by. 1965. Most. Schools had become co-educational. That. Included. Harvard. UPenn, many. Of the schools in the West such as Stanford Caltech. Etc, and about. Overall, I think 84 percent of colleges. Were co-educational. So by the time ye'll actually, became co-educational in, 1969. They. Were far. Behind most of their peers this was not necessarily, a, great, achievement, but, nonetheless, they. Had a very large transition, to make shifting. A school from Seoul email to a co-educational, institution, is. No easy transition and that's really what we wanted to focus on through, the eyes of Margaret Homans herself, and looking at her experiences. So. With that in mind. Margaret. Howe Muntz starting. With her childhood, was born in Boston and, she. Actually attended, something. Called the Commonwealth, school which is a small, private school in Boston, and she, credits that with sort of her ability to get, into a place like he also early become one of the first members of her class obviously, she had privileges that many, other did not many other women did not have during. That era and she. Actually taught it she, decided to attend you not. Really because it had just become co-educational, but just because of the strength of its academic programs she, had a teacher, in high school who had, gone to Neal for English. He sort of described her sort of a utopia, for the English language and studies of it so, she chose you mostly, just for its academic, achievements. And the fact that it become co-educational, allowed her to do that but, it was not that she desire to be a pioneer she simply wanted to study English. So. Yale, to give a very very brief history given the time constraint, decided, to admit women in the 1960s. After, a variety of student protests. So. The main factor, that shifted, during this era was that Yale's actual, admission criterion. Began to shift president. Kingman Brewster decided. To. Begin to admit people based on merit, as opposed, to family, connections, wealth. Etc which, seems, of a very obvious decision to us at the time but. Earlier. In this era merit, had been a factor, but not the main factor and admissions, and so, once merit, became the true determining. Factor between who was admitted and not admitted then, many more people from public institutions, public. High schools Western. High, schools began to attend Yale and that's, what had produced the demographic, shift amongst the men at Yale and.

Eventually. That led to protests. Into. What's called Mayday, which. Was a series, of protests. That. Tried. To show. Akemi. And brewster that the student. Body was behind this shift towards co-education. So. Upon. Feeling. A lot of student pressure faculty, finally decided to get behind this and they decided to create proposals, etc. And finally. In 1969. Ye'll, decided to become co-educational. And again. To establish some, context, this. Shift was not easy we included the multimedia, here of letters, that were sent to kingman brewster. Some. Calling, women indignant. For, showing. Their knees and thighs and public. Calling. Them prostitutes, if they were to even show any sort of skin many. Letters saying that if women dominated. The a leo would end you. Know they, wanted their sons to being able to go to Yale and they, could not do that if women somehow attended. So. Well the time hormones, got here it, was not exactly a, easy, transition. And I, think that's really reflected, in her anecdotes, and her stories so. Starting with academics, she, describes, being, used to being in a, simply. Kawatche occasional, environment, to her it was not significant. That women, were now in these classes but to many of the boys it. Was she described being in a classroom especially. In her English classes and being one of only one or two women there and how, the pressure was always on her to speak whenever any topic, related to femininity, came up and how. Outside, of that she felt very reticent to speak she did, not feel as though she could really develop a voice. One. Of the things that she described was lucky was being in classrooms, with some. Female professors, who actually encouraged, her to study, female, literature, and. Feminist. Critique and she felt like that was really the main academic, experience, as. Opposed to reading just books like what she described as and I quote dead white men. Outside. Of the classroom Hmong's became, involved heavily in the women's, abortion, referral center, at Yale which is currently in the, it's. In the it was formerly in what is now the a fam house if, any of you know where that is she, also briefly, joined the glee club and several, other clubs on campus although. She felt like she was usually admitted to those clubs solely because she was a woman and most of the clubs at the time had a quota. Where they were supposed to try to achieve equal. Gender balance so there was a lot of pressure to admit women. Some. Of the experiences, that I thought were most poignant. However, were to sir personal experiences, so. Women. Were all housed in Vanderbilt, Hall during. This era and there was a glass door to Vanderbilt hall and Hmong's. Described that door is being symbolic where she felt as the women could see, sort. Of the freedom they were promised, in co-education, but they couldn't actually achieve, it because there was a guard at that door who, wouldn't let them out at night etc whereas.

None Of the other halls had a guard or such, a door. Another. Experience she mentioned was how, she sewed, herself what. She described as a uniform which is essentially a long dress and she, wore that every day to essentially cover up any part of her body because, she was not used to being sort, of viewed as a female body as opposed to just another student and, she wanted to hide herself and this, uniform was a way for her to do that and. Finally. She actually described, how she refused, to. Walk. Down. The. Graduation. Hallway, in, her cap, and gown because, she felt as though that was encouraging, in all-male, tradition, and she wanted to rebell and on. The whole I would not describe her dream Suzanne is easy but. She. Was able to adapt to Yale in fine pencils where she was comfortable, this. Heat, map shows, areas on campus where, she felt. Comfortable. And uncomfortable and. By. Sort of carving out certain areas on campus she, was able to sort of adapt find, other women and in, create an environment in which she could learn but. Obviously looking forward, to her experiences, I think are emblematic of the, path yourself to go to achieve, trucco. Education at Yale and I just want to leave you with the idea of like what does co-education, mean is it, truly just having women in our classrooms, or is it allowing a fully. Immersive, and equal, educational, experience, and how can we achieve that thank. You very much. Thanks. Very much to Jasper, and now, I'd like to invite Nina warszawa, up who is going to speak on the relationship, between the style and impact, of judicial, opinions, okay. First of all thank you so much to the digital humanities lab for all of the help they've given me all, the support they've given me with my research and thanks, especially to Doug Duhon who has helped me tirelessly. With the minutiae of this project. And related work I'm. Incredibly grateful I'm. Going, to be talking about the. Relationship, between the style and impact of judicial, opinions, so. Judicial opinions are the texts, that judges, write in order to explain and justify their. Decisions, and these, texts, make up the common law when, judges decide new cases they, have a legal duty to follow the rules and reasoning, set, out in previous opinions. The. More cases that, a previous, opinion, appears in the more influence, it has on the common law so. I examine. Whether and how an opinion, style, is related, to its impact to. Its legal impact. My. Results, show that if that an opinion style is a significant. Predictor of. Its. Impact, where impact is measured by the number of cases that will cite it. So. To give you a brief overview of my, approach, my. Data set includes, almost all. Majority. Opinions, from the 10th Circuit Court of Appeals during. A 12-year period this. Court is an important federal appellate, court which answers to the US Supreme Court it's also the court where Neil Gorsuch, served as a judge before. Joining the Supreme Court last year, so. First I derived a set of measurable, stylistic. Features from, the literature on judicial, writing I then, used natural language processing to. Detect. And measure. These features in each of my opinions. My. Dependent, variable is, the number of cases that cite to the opinion and I, use this as a measure of an opinions impact, on case law or its power as a precedent. My. Independent, variables. On the top I've listed my stylistic. Features. Of interest. On. The bottom are my control, variables. For. The most part on the stylistic, features I measure these on a per word basis. Meaning, that for example with citations to Authority. I counted, how many times an opinion, cites statutes. And other cases, per word. First. I'll give you an overview. Of. My. Data. Using, some charts to give you a sense of what. They look like and then I'll show. My statistical, model, and my statistical, results. So. Here you can see how judges line up on lexical. Diversity. Lexical. Diversity, is basically, the range of vocabulary of. An, opinion. It's. Simply, the number of unique words over, the total words so. You can see here that the, judges range from about 20% unique words to. 28%. Unique, words, male. Judges are in blue female, judges are. In maroon and. Gorsuch. Is highlighted, in red. Here's. Informality, this is basically informal, usage per word and. You. Can see here that the judges range from about zero. On informality, to, about point zero one which means one per 100, words is informal, with, Gorsuch at the high end. Here's. Certainty, so this is certainty, terms. Expressing. Certainty, per word. So. Judges range on this from about one to three point five certainty, terms per, 1000, words and, you.

Can See the female, judges. Clustering. On the low end of certainty what's interesting is they also cluster, it on the low end of hesitancy, so, these this represents. Hesitant, or tentative. Expressions. And Gorsuch again is on on the high end of this. So. Here is my main. Statistical. Model. This is a mixed model which is a type of regression, that, suits the structure of my, data to. Give. You a summary in my results show that stylistic. Quality is run with the cases, impact, or presidential. Power and systematic, ways. My. Style variables. Are on the top and the blue. Rectangle. All. Of. These. Variables. All these stylistic features, with the exception, of passive, voice our. Statistics, statistically. Significant, predictors, of citations. And recall that my dependent, variable is the number of cases that cite the opinion. Most. Of these factors, are positively, related to. Citations, but. Two of them certainty, and informality, are negative, predictors, of citations. So. I'll take my remaining, time to talk through these results since it's, not altogether obvious, I think just from looking at the values what they mean because. For one my dependent variable is log transformed. So. As word, count goes up by one citing, cases goes, up by, sorry. As we're count goes up by one percent, citing cases also goes up by about one percent as reading. Level goes up by one grade-level. Citations. Goes up by 5%, as. Lexical. Diversity, goes up by one percent sites go up by two percent, as, Authority. Per word goes up by point zero one so this means one additional, citation. To us to, a case or statute, per, 100, words cites, goes up by 27, percent. As. References, to people by proper naming goes up by 1 in 100, citing, cases goes up by 20 percent and that's, the same for references, to, lid against by technical, terms plaintiff, or defendant so. It seems that just referring, to people regardless of how is a good thing for citations, a certainty. Per word goes up by point, zero zero one. Citing, cases goes down by 4% but. As hesitancy, goes up by the same amount, one. In 1,000, sighting. Cases goes up so certainty, is a negative, predictor, of citations, but hesitancy, is a positive, predictor. Semi-colons. Goes up by 1 in 1,000 sites goes up by 3% and then, as informality per word goes. Up, by. 1 in 100. Citing. Cases goes down by 20% so, informality. Is, strongly. Negatively. Related with, citations so, to just, wrap up I also ran a regression with a normal regression, with judged judge, fixed effects which generated, very similar results to this model the, fixed effects model. Shows. That my variables, explained 45, percent of the variance and citations, and that. My stylistic, variables, alone just these 11 stylistic, features explain 15%. Of the, variance in the number of cases that will cite an opinion thank, you. Thanks. Very much to Nina and now I'd like to invite up participants. That our project called digital humanity. Jessica, Embry ambrosio, and Maria. They're gargouille. Julia apologies, for that. Good. Morning everyone my name is Jessica Morrison and I'm a junior majoring in computer science, hi. My name is Maria gradual oh and I'm also junior, majoring in statistics, in data science and Spanish and where the co-presidents. Of Yale code for good today, we're presenting on what we call digital humanity, and, we'd like to begin with this brief anecdote, the. First program most people write is a simple, hello world it's. Time we start seriously considering, and talking, about the, impact that our code can and does have on the world in which we live. So. As a club we have come to defined coding, for good as applying, software tools and techniques to solve problems that our community or society, faces, so. What this means is that we. Stick to design and develop programs, and our only address, the technical aspects, of problems, but also consider, the impact that these technologies will, have on the people who make use of them is the impact positive, does it benefit, them beyond just, merely solving, a technical issue.

So. You, might be asking now how. Do you do that. So we, do this by recruiting, teams of four to six students, to work in collaboration, with. Nonprofit. Organizations, yell student groups or local government. Agencies, to address socially, conscious problems, and these can cover all types of different things there's, no one problem. That is or is not socially, conscious many things are different in many ways we. Currently have three project, teams two of whose work we'd like to highlight today. So. Our first project, is the, road sign manager, and for. This project we partnered with make him in a locker maker space in downtown New Haven and then you have any permanent transportation. To address transportation traffic. Issues the, main problem to solve here was that commuters, did not know where how many parking spaces were available at a given location, to. Solve this problem make even modify the Hardware of multiple, road signs so that they could be programmable, nevertheless. Someone, still had to go down to the road sign and change it manually. To. This and our team built a web application that connects the road sign so that officials, can simply access the web application, and update. Any information on the road sign in real-time in. Addition to improving the parking system in New Haven this software also provides a great mechanism for, important, updates in case of emergencies, if, you want to hear more about this project the team who worked on it will be later presenting, in during the poster session. The. Next project that we'd like to talk about today has been done. In collaboration with ku baby so, cushy baby is actually a nonprofit organization founded, at Yale started. In the CID as part of a class, project for I think a mechanical engineering course a few years ago it, operates, in India in, founding, their organization, the co shoe baby team sought to address. Issues in the indian medical system currently. Doctors. Are required to keep paper records, an. Issue with these records they need to travel with the doctors as they move they. Often become just organized and, files. Get lost and this is particularly problematic for. Women and their newborn children, the, issue with this is that they need to see doctors at certain points post birth and if. Doctors, are losing files there's. No record of which vaccinations. They've received when, they've gotten past appointments, so. To deal with this because she BBT and made a simple wearable, it's a little necklace that the child wears and it stores all their information in it when. Doctors go to see the children and their mothers they can simply load their data, into a web application if, they have internet access and if not there's, an Android app multiple. People can view the information at. When they're seeing the child I'll fly, whenever. They need to and. This is really helpful because it, deals. With the missing record, issue and also lets. People look at information, in. Various places the. Code for good team has been contributing, to this project by helping because she baby updates some of their models one, of the big things that the team cares about is risk assessment, they. Like to triage their patients, so that newborns. And their mothers were particularly, high risk for, various types of health issues can be, viewed. According to the relevant part. Just a little image of what the dashboard of because she be the application looks like.

Now. You might be asking yourself why. Do we do this why I was quit for good found it so, code, for good was founded last spring, the. Reason it was founded is because there was no real space available, on campus for, technically, inclined students. To work, on socially conscious problems, so, there, are a lot of wonderful places where our students can go and work out but. The impact, that they can make their will really, reach the people who need our help the most. So. This is your typical job, bird these, are the kind of companies, that we usually see that any CS, or data science major will, look at and you. Know what these are wonderful, places to work out they to work at they, are doing amazing stuff but, the, only present, a personal, image of what can we do with what can be done with technology. And. To. End off today we'd, like to leave you all with a question or. Perhaps, a challenge how, can you do more good with the technology, that you use or know how to use. So. If you have an answer to this question or, just want to share your ideas with us please come to us at code for good at Yale dot e-d-u thank, you. Thank. You Jessica and Maria and to all of our presenters during this first initial lightning, talks a part of the conference I think at this point I'd like to invite up members, of our roundtable, discussion. You.

2018-08-12 17:06

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