Tr : 18 March (Jamie Joyce)

Chat Log: https://futuretextlab.info/2022/03/18/chat-18-march-2022-jamie-joyce/

Jamie Joyce: Thanks again for having me, it’s really lovely to be speaking with all of you. Some of you I already know, which is pretty cool that you popped in today. And I just want to say also, I think it was Fabien and who was it, Carl? Was it you who mentioned future cities? And, Fabien, you mentioned VR interest? I got to say, I’m so fantastically interested in both of those things because at The Society Library, we’re extremely interested in researching different ways in which we can visualize knowledge. And I always can’t help but go to the VR space and think about how we can make inner direction much more multi-dimensional and even physical and kinetic. So I’m so excited about that, even though we’re not working in that space, I want to get into that space. And then also, when it comes to Future Cities, The Society Library has also been recently working on creating decision-making models for city councils. So, I’m going to talk about our work in general and talking about all these different projects that we’re involved in. And if it’s not too gauche, I also have visual aids, and I can flip between screens to show you what everyone what I’m actually talking about if that’s all good with you. 

Frode Hegland: Well, hang on. You said if it’s not too gauche you have something, and I couldn’t hear the words. 

Jamie Joyce: Oh, I have visual guides like presentations and tabs open. 

Frode Hegland: Yeah, excellent. Absolutely. 

Jamie Joyce: Okay. In case any of you don’t know who we are, we are The Society Library. We’re non-profit, a collective intelligence non-profit. I’m going to start this presentation just by talking about who we are and what we do. Then I’m actually going to show you what we’re up to. And I’d love to store some of your feedback and some of your ideas because some of you have been thinking about these types of projects for decades, and I’m only three years in. I have been thinking about it for about seven to ten, but I’m only three years in, in terms of implementing these things. So I’d love to get feedback and to hear how you think we could grow and expand what we do. So, we’re The Society Library, the main projects that we’re working on are essentially:

How can we model societal scale deliberation? 

How, in modelling societal scale deliberation, we can actually start creating ways in order to have more informed, inclusive, and unbiased decision-making? 

How can we generate policy more collaboratively by taking in the inputs of individuals? And then, ultimately:

How can we have a common knowledge library on complex social and political issues that can inform the general public?

So all of these are very like pro-social, pro-democratic projects. The Society Library itself is attempting to fill a role in society. In the United States, we have something called the Congressional Revenue Service, it’s run by the Library of Congress. In the U.S., the Library of Congress actually prepares all of these lovely briefs and does all of this research for our members of Congress, our senators, and our house representatives. However, if I’m not mistaken, at the state and local level, as well as for the general public, these types of research services don’t exist. So our congresspeople, they can make a request to be brought up to speed on the topic of AGI and an entire library will work to organize and do all this research to deliver the knowledge products. And the Society Library is looking to do that for the general public, and also at the local and state level in government. So some of these projects that we work on are deliberation mapping projects, like our great American debate program, which I’ll talk about, specifically. We want to get into a project which we’ll probably rename because everyone hates it. I called it The Internet Government, people assume we’ve been governing the internet. We certainly do not mean that. It just means enabling governance platforms on the internet. So how can we generate policy? How can we produce decision-making models that are informed from the collective input and deliberation of? Essentially, what we’re aiming for is an entire nation. So we’re really working at the societal scale and I will talk about how we do that. And then, ultimately in our timeline, we want to fill this role as being accumulative common knowledge resource for the public. And so I’m going to talk about where we’re at right now, which is model and societal scale deliberations. And I’m going to get into how we actually go about accomplishing that. 

Currently, the topics that we’ve worked on, that we’ve cut our teeth on, in the past few years have been the topic of nuclear energy, climate change, COVID-19, and election integrity issues. We’ve mapped a few other spaces, as well, essentially, associating actors with actions in political movements. So, for example, we were mapping these kinds of things out in the George Floyd protest. But what we’re really interested in is finding what are the fundamental questions that society has about specific issues. Currently, we’re working within the English-speaking United States. And then, can we go ahead and deconstruct the collective knowledge content that we have, to go about compiling answers to those questions so that we can compare, and contrast what propositions, what positions, what arguments have more or less evidence. What kind of evidence, how much evidence, produced at what time in corroboration with what. So we’re really interested in just being able to start visualizing the complexity of our social and political discourse. So that, again, it can, down that timeline, start forming decision-making models in the production of policy. So, if it’s not obvious, to me what we discovered when we undertook the project of, Okay, let’s start mapping these debates” is that, debates in the United States, on these high-impact persistent polarizing issues, are actually unbelievably large. So the topic of climate change, I think we’re now up to 278 unique sub-topics of debate, and there can be tens of thousands of arguments and pieces of evidence in each one of those sub-topics. But interestingly, what we found is that all of these sub-topics correspond to answering only one of six questions. So all of these debates that are happening across various subjects related to conceptualizing the problem of climate change and its severity, to solutions, and things like that, all of them are really responding to six fundamental questions. Our latest subject that we’re working on is nuclear energy, and we’re still assessing what those fundamental questions are. For COVID-19, because it was such a new subject, and I think it was so global and so viral, we found over 500 and basically 13 fundamental questions. And for election integrity, there were 81 subtopics and two questions. Today I’m also going to be showing you a project that we’re going to be releasing at the end of this month, which I’m really excited about. I’ll actually take you through the data structure and tell you more of what all of this actually means. I’ll show you questions, I’ll show you sub-topics, etc. So, how do we go about creating these debate maps? Which, again, I will show you what they actually look like towards the end. We have this process that we’ve developed. Essentially, it starts with archiving and collecting mass content, transcribing it and standardizing it to text, extracting arguments, claims and then evidence and categorizing those, clustering those into hierarchical categories, and then, inputting that structured content into a database, and then, tinkering with visualization so that, essentially, we can compress as much knowledge as possible in visualizations that can convey the complexity, without being way too overwhelming. So, something that we’re really interested in is: How do we create knowledge compression where people can see as much knowledge as they want to see and they have the flexibility to work in various dimensions to unpack what they want to explore as they want to explore it? So instead of an author, who’s writing a book or a paper, taking a reader through a specific narration, instead it’s about: How can we visualize all the possible narrations that a reader can go through and they can unpack in the direction that they want to? And I think we’ve had a recent breakthrough in how we’re going to go about doing that in a really simple way, so I’m really excited about that. I’ll show you a tiny little sneak peek of it. But most of it is on wraps until we launch the project at the end this month if all goes according to plan. So that’s the basic process, but let’s get specific because that kind of matters. So when we talk about archiving what do we mean? Well, first we built a bunch of custom search engines, to essentially, make sure that we’re pulling from all across the political spectrum and across different forms of media. We also have curated feeds that we keep an eye on. We are also aware that it’s really important to break the digital divide. There are just some things that people are not going to be willing to write a medium post about or post on Twitter about ideas that they have, that they do want to express. So, facilitating conversations and recording those interviews with permission, in order to gather that audio data is very important. We’ve also acquired access to various searchable databases, GDELT, and Internet Archive, which have just been absolutely wonderful to us. Sorry, the screens are kind of in my way at this point.

Frode Hegland: By the way, Jamie, we are in no hurry.

Jamie Joyce: Okay. Well, I just talk fast. It’s kind of like…

Frode Hegland: No, no. Your talking is fine. It’s just you seem a bit stressed about the screens and stuff. We’re just having fun, and this is amazing to listen to.

Jamie Joyce: Okay, cool. Thank you. All right. So now that the screen is in my view again, one thing I do want to emphasize is that The Society Library takes very seriously our research methods. We have over 22 methods that we developed to try and counteract our own research biases. On our own echo chambers, we have a list of policies, also, on our website that talk about all the wicked problems of knowledge management that we’re seeing. So when it comes to inputting content in the database, are we going to platform a dog whistle that we’re aware is a dog whistle? Are there going to be policies about trying to referee if one group is calling another group’s language a dog whistle when, perhaps, it isn’t? There’s all these wicked issues that we care very much about in addition to the methods that we’ve already deployed in order to overcome our own biases. And we’ve got a virtues and value page related to how we see ourselves in relation to knowledge. So, when it comes to us digging and scaring around, and archiving all this content, how do we feel about misinforming and disinforming content and things like that it’s, we like to approach our work with total intellectual humility, and those virtues and values are listed on our website. So we talk about that often in the culture of the Library, we often hire librarians who have their own code of ethics, which we also appreciate very much. The librarians in general, in the United States, take a very anti-censorship stance and instead argue that, if there’s the right context, any information can be interacted with to enable enlightenment. It’s not that some information should be hidden away, but what is the right context for knowledge to be experienced so that it informs and enlightens, rather than corrupts and persuades. So, we have developed our own code of ethics. We are also members of the ALA, so we adhered to those rules as well. I just love to say that. 

And then the process also begins by this, kind of, flyover, we call it, where we quickly find a whole massive topic, this can be done manually or computationally, where we just collect a bunch of topics across various media types, and we do that by specifically targeting and grabbing sample collection across different media types that contain certain keywords that are related to certain topics. So, if we’re mapping something related to nuclear energy and tritium leaking into the environment, which is a radiological hazard is one of those topics, our archivists and librarians are going to go through and find where does that keyword happen in podcasts, books, definitions, and government documents, etc, not only through time but also from news articles that are across the political spectrum. So once we have that diverse set, that’s when we do the next step, and let me just quickly say where we collect content from, scholarly, articles, research papers online, news, websites, blogs, social media, Twitter, recently Tik-Tok, Facebook and Reddit, we pull from documentaries, videos, and television, topics, specific community forums, and groups conference, videos and summaries, government publications and websites, existing FAQs and online resources. And then, we also conduct interviews with industry leaders, thought leaders, and experts. And pro-tip, so many government agencies in the United States actually have in-house librarians and they are so helpful. We just send them our research questions like, “Hey, can you look through your entire agency’s library and help us find the relevant documentation?” And oftentimes they’re more than willing to help. So that’s just really lovely. Anyway, once we grab the sample sets of data, we deconstruct all of that into text, we transcribe it, we translate it, we parse it, sometimes we even hire people to actually type up descriptions, text-based descriptions of videos, and graphic imagery, so we have that text, so it’s searchable. And then, once we have that content, what we do is, we start deconstructing the arguments, claims, and evidence. So we’ve got a training program for that. We have our own standards for what we mean by claim. Drive claim, implied claim, implicit claim, argument, etc. And I’m going to show you what deconstruction looks like just because it’s good to know what we’re talking about. So this is a transcript from a Sean Hannity clip. It’s an old example, but it’s one of my favourite examples, because one of our favourite people at the Internet Archive asked us, specifically, to deconstruct this, because they couldn’t believe that Sean Hannity would make all these claims. I think this is only about like 17 minutes in length, yeah, it’s about 16 minutes and 20 seconds and all the claims that we were able to extract, I think, let’s see here, 100, oh, the implied ones are hidden away, so, in terms of directly derivable claims, Sean Hannity made 179 claims in his 17-minute snippet, and there was way more implied, I accidentally pulled up the wrong example, so sorry about that. I’m not an excel spreadsheet wiz, so I don’t know how to unlock the hidden implied claims. But anyway. From the exact transcript, which is right here, we actually pull out the arguments and claims directly, so oftentimes, you can see that there’s one line right here that can actually pull various claims from that. And that’s because language is complex. It’s dense. And we really want to extricate all of those tiny little fundamental units of reason, because we actually want to fact check it, qualify it, debunk it, stillman it, devil’s advocacy, at all these things. Another example I’d like to show, this is the Green New Deal. The Green New Deal is a famous legislation in the United States. When we’re training our students, because we have various educational internships, we’ve worked with 32 universities in the United States. One of the training projects the students do is, they deconstruct the Green New Deal to, kind of, get a handle on what the Society Library standard for clean is. So I think there are 438 claims in the Green New Deal. It’s a very short piece of legislation. If you just copy-paste it to a Google Doc, it’s like, 13 pages, I think, in a relatively big font. So it’s a relatively short documentation, yet, there’s 438 claims. And there’s very little evidence that’s usually provided in the legislation. It usually qualifies itself within like the… It actually does have an interesting structure. It’s a very complex argumentation. It has a premise where it says, “Congress fines given this document.” Which they reference the IPCC report if I’m not mistaken. These are our findings and our conclusion, which is the recommendations by congress to create a specific policy program or whatever. So, yeah. We deconstruct pretty far, I would say. And then, going back to the presentation, what happens when we have all of these claims? That’s when we start categorizing them. So these claims are going to have keywords, those keywords are going to be semantically related to other keywords. There’s ways in which, I hope in the future, we’re going to be better at computationally clustering these things together. I’m really interested not only in taking the data that we’ve created, using it as training data for claim mining, but I’d also like to start seeing if we can generate syllogisms just by the relationship between the keywords in text snippets. So that, potentially, with enough training, maybe our analysts would have more of a fact-checking role than constructing arguments from the base claims role. But technically what we do is, we categorize cluster claims based on the relatedness to specific topics. So they may be, for example, on the topic of nuclear energy, it could have to do with grid reliability or stability, the tritium leakage, other radiological issues. We just cluster those into categories, and then from those categories, we’re able to derive different positions and more complex argumentative structures. I’m going to show you what some of that looks like in the debate map as we go on. And I will say also that we’ve been very fortunate to have a very large tech company, who we’re not allowed to name, and a lovely university who we’re not allowed to name, who have made fantastic argument mining technologies, and they’ve given it to us to use. But we’re a small non-profit, so they’re like, “Yeah, don’t tell anyone we’ve done this.” So we also have interesting argument mining tools and we’re hoping that the training data we’re creating can make things even better. And this is just an example, again. In this one natural language text snippet, we can pull all these claims. A derivable claim means that we can, essentially, use the same language in the text snippet and just cut out some things and reconstitute it, in order to create claim. When an implied claim is that you have to have some, sort of, insight into the meaning of the claim itself, which requires human intelligence in order to suggest that this claim would have to be a part of the argument, or one of the premises of the argument, in order for the claim to be proven to a certain extent, or made sound or valid. So our analysts are also trying to put in implied claims but also mark them, so they don’t get confused with things that have been derived from evidence or from sources. That’s just an example of that. And then, understanding our hierarchy is really important too. What we found is that if we just randomly choose a question, or randomly choose some dimension of a debate, what happens is, as we’re hand mapping the logical argumentation from that one point in the debate, we start to quickly get into a spaghettification problem. So we start having arguments that are somewhat relevant, it just, kind of, spiders out, and curls in on itself. It’s very messy. From what I’m told from people who’ve worked in AI for a long time, it’s called a good old-fashioned AI problem. But what we’ve discovered is, if we just do this kind of hierarchical clustering, over time, essentially, what we can do is have this descriptive emergent ontology that occurs. And what’s interesting is that the questions that are derived from finding the references and evidence, extracting arguments in the claims, organizing those into those categories, and then, identifying those positions. In finding those questions, the questions, in turn, shape the relevance of what can be modelled in response to the question. So if we’re interested in having the most stillman formal deliberation possible, it’s the responsibility of our analysts to make sure that the positions are actually answering the question. So finding the questions that give shape to the relevance of the argumentation, which has really helped us to avoid that whole spaghettification problem. I don’t know if it solves that good old-fashioned AI problem in general, but it’s just something that has really helped us. We call it descriptive emergent structuring. And we’ve used it on all of our debates since we learned the first few tests that, just picking a random part of a debate isn’t going to work for us. 

And then, we go ahead and map this content. We have a debate mapping tool. Every single week we ship new features and make it even better. Some of the things that we can put into this debate mapping tool includes, question nodes, category nodes, argument nodes, we can create multi-premise arguments, unbelievably rigorous logical proofs. There’s claim nodes. We can also, within every single node, have definitions, variant phrases, videos, images, media, equations, references, and quotes. There’s ways in order for people to participate in calculating impact and veracity scores. And then, every single claim and node can have a pro, con, truth, relevance argumentation associated with that, as well. So we’re able to have a pretty complex argumentation that’s mapped out using this specific tool. And then, of course, we want to model it. And like I said, of the knowledge products that the Society Library produces, creating these maps is just one thing. We’re really interested in creating a much more accessible visual libraries apps. I would love to put it in VR, how fun would it be to stretch and open claims, to unpack them? It would be lovely. I would love to take some of our data and maybe work with someone who would have an interest in visualizing these things. I think it could be lovely. But the other things that we do, as I mentioned, we create decision-making models, so we’re moving into the smart city space. We’re trying to pitch it like, smart cities could be smarter if they had ways to augment their intelligence by externalizing the decision-making process. Some of the work that we’ve already done, I mentioned earlier that, something that we’ve discovered in our work is when you really map a space, you find out how dense and complex it is. So a city council wrote to us, and they asked us to help map a debate that they were having locally. And they thought it was a binary issue, like yes or no. And we found there were over 25 different dimensions of the decision that they were facing. And there were anywhere from two to five arguments with or without evidence in each one of those dimensions that they would consider. So we created a micro voting protocol that essentially allowed them to zero in on one dimension at a time. And the feedback that we got from that project was just so wonderful. People who felt like they were being marginalized, felt that they were heard. People who felt like they were on the fence, we were finally able to see that they were actually really certain of a specific position, they just need to externalize it. So we’re moving in that space, and we’re pitching to various city councils to, essentially, not only do research work for them, like the Library of Congress does for congress, but also create these decision making models because that can increase transparency, accountability, and decision making, and it helps overcome a little bit of cognitive bias. Because if we’re making decisions in our heads, who knows what kind of black-box calculations and waiting is actually going on. But if we’re forced to externalize it, and work with things one-on-one, and actually identify what dimensions we’re in agreement with, it just, I think, helps improve the process in some way at least a little bit. So we’re hoping that we can just improve decision-making in general. And then, we’ve also been hired to create legislation. So using our method of deconstructing content down to the claim level, we produced federal level legislation. We essentially took hundreds of pages of congressional recommendations, broke them down to the claim level, and then, we compared those with legislation that was passed, failed, and pending at the state and federal level in order to, essentially, say, what, in the congressional recommendations, on this specific issue, is missing from the existing legal code at the federal level, and then, borrowing language from where it’s been attempted in the past, and produce a bill, just essentially, by claim matching, and filling in the blanks. So we were willing to work on something like this because it’s a very non-partisan issue. It had to do with the infrastructure bill. It had to do with the integrity of the electrical grid in the United States and recommendations to harden or make it more resilient. So it’s a totally non-partisan issue, we were happy to do it. And we also got amazing feedback from that work. We were able to, literally, deconstruct hundreds of pieces of legislation, and hundreds of pages of congressional recommendations in under three weeks, and deliver this proposal. So, most likely, we’ll continue doing non-partisan legislative work. 

And then, also, we think that some of the data sets that we’re creating may actually be very useful training data, not just for us, but for other people as well. We’re thinking about that, also, being a potential revenue stream as a non-profit. Oh, one more thing. I mentioned before that this debate mapping software has a lot of different features. We can pack videos, images, quotes, equations, references, and all these different things in a single node. And so, when we made a submission to the Future of Text book, it was about our concept of web-based conceptual portmanteau. I’m going to show you a little bit of what that looked like when we were initially mocking it up, and then by the end of this presentation, I’m going to show you where we’re at currently because I think it’s a huge improvement. So our first foray into web-based conceptual portmanteau is, we were, essentially saying, portmanteau, when you combine a couple of words together, and it’s like this more complex meaningful word. And we’re like, “Okay, well, we’re trying to create portmanteau in terms of media assets. We want to combine references with images, videos, text and different variant phrases of the text, and all these different things.” So we mocked up this new node structure. And we were talking about different ways in which people could unpack and repack all of that knowledge. I’m going to show you our debate map and take you through a little bit, and then, I’ll show you the new version of web-based conceptual portmanteau, which is still being a little like tinkered with currently. I’m happy to show you a little bit of what we’re doing. So here’s an example of one of the debates that we’re going to be pushing out. This is the data staging area, so you’re very much seeing the behind-the-scenes. These are the tools that our data brains and librarians use to input structured knowledge. I’m going to show you how complex it gets. So we were asked to map the deliberation about the Diablo Canyon Nuclear Power Plant in the United States. It’s the last one in California that’s in operation and there’s a great big debate about it. I think even Elon Musk has alluded to it in Tweets. So it’s a high-profile issue for the state of California, and we were very lucky to be asked to start mapping it before it really blew up. What we’ve found so far is that there are generally like two fundamental questions that the community has which is: 

What should happen to the Diablo Canyon Nuclear Power Plant? 

And why is it being decommissioned? 

I put little trophies as notes to myself so I don’t get lost. And so far what we found is there’s about seven different positions that the community is taking on these issues. And by community we mean academics from MIT, we mean the governor, we mean activists, we mean members of the local community. Essentially we had an interest in finding all the different stakeholders, checking out the media that they were producing, and then, extracting that media. This data set, I believe is drawing knowledge from 880 different media artifacts. I have a list here. In this database, there are references to 52 knowledge from 51 scholarly articles, eight TV segments, 112 reports, five books and textbooks, 367 news and websites, 194 social media posts, 66 videos, 24 podcasts, 53 government international documents, together for 880. And so far what we found is, there’s seven general conditions one of those is that it should just be left to be decommissioned as scheduled. And then, we see that this breaks up into a variety of different categories. So there’s economic issues, environmental issues, safety and well-being issues, ethical issues, all in support of why it should be decommissioned to schedule. So, as we unpack this, we can actually start seeing some of the reasons that people pose for why it should be commissioned. And you may notice that there’s these little brackets right here. Those brackets are important because, in order for us to translate data from this data set to the visualizations that we’ve created, we’re actually substituting these lines, which are indicators that the relationship between nodes with this language. So we’re doing this, even though we’re just creating this knowledge graph structure. And these lines, their colours, and orientation are indicators of their relationship. We actually will substitute with that language, so it’s completely readable (indistinct) visual. 

Some of these claims in support of it being decommissioned that are related to economic issues include: that closing the plan won’t harm the local economy as much as previously thought, and that market forces have made the Diablo Canyon Nuclear Power Plant redundant and uncompetitive, so we can unpack this even more. And again, in brackets means that we’re essentially identifying the relationship between these tech snippets, other tech snippets, so as we move on from this one which won’t hurt the local economy as much as we thought, the economic impact of the local economy would be smaller than previous estimates in part due to economic resources that we made available, and over time, the region will overcome economically and experience positive growth. Now I’m just going to take a moment to start unpacking a little bit of what’s available in this node. So what you’re seeing is this text and these references here. What we also actually have is a bunch of different phrases, a bunch of different ways of expressing the exact same point in a similar linguistic register. So we’ve identified that as standard. We also want to make all of these nodes really accessible to people with less subject matter familiarity and can handle less cognitive complexity. So this simple version uses much more simple vocabulary. It says, Even though it seems like there will be a lot of bad economic consequences when it closes down, some experts think it won’t be that bad. So it’s a very simple way of expressing the same thing. And we have some more technical versions of this claim that actually refer to the economic assessment. Why that assessment was commissioned? What type of specific input-output modelling tool was used to derive those conclusions? It’s a much more technical way of expressing that same claim. And then, in support of this particular claim, we have a multi-press right here which actually breaks, this is the summary of the multi premise which we can unpack here that actually breaks down the logical argumentation that led to this particular conclusion. So every single stage of, not the experimentation but the conclusion, so the first step of the decommissioning process will result in this kind of economic growth, and we’ll have economic losses, then we’ll have economic benefits. Overall it will conclude that. And this can unpack even further because each one of these premises in this argument can also have pro/con argumentation that can argue whether it’s true, support it, or its relevance. We have an example of a relevance argument here, which calls out that the economic impact assessment didn’t look at the economic impact after the decommissioning of the plant was complete. So when it talked about positive economic growth, it was only so far out of a projection and they didn’t include certain things in their model. And, of course, this comes from news articles. So it’s about collaboratively finding where the conversation is happening, distributed across different media artifacts, and then bringing that argumentation actually close together in one spot for people to explore. I’ll show another little example and there’s tons of these. Based on market forces demand for CCP nuclear energy is expected to decline. We have another multi-press here which essentially breaks down all of the different economic trends that are happening, which have been collaboratively expressed. So it’s not just that energy efficiency policies are going to reduce overall electricity consumption, there’s also increased solar then CCAs also don’t prefer to buy nuclear energy. And as you can see, it can get pretty dense pretty quickly because all of these are unique arguments that support these more vague generalizable premises. So the more that we go upstream and go left, the more vague the statements are, and the more interested someone is in to, actually, digging into the argumentation and evidence that’s available to support these vaguer, high level, commonly expressed sentiments, people can really dive in deep and explore. But of course, this begs the question: 

Why on earth would anyone do this or want to find knowledge organized this way? 

And even though when we publish this collection, we’re going to have a viewable version of this map accessible if people love to explore this map. Hopefully, we’ll deal with the functionality a little bit, we will centre things and make it a little bit better. But we know that this isn’t really going to cut it, in terms of making this accessible, legible, etc. So what I’m really excited about, going back to the web-based conceptual portmanteau, we’ve been wondering, how can we take all of this knowledge and really compress it down? So I’m gonna show you a sneak peek, I wasn’t originally gonna show up because this is recorded and I didn’t want this to end up on YouTube yet, so I’m just going to show you one image and just give you a little taste of it.

Frode Hegland: Tell us when you post it to YouTube, it doesn’t have to be immediately. But just give us a date, afterwards.

Jamie Joyce: Okay. I mean, I’ll just give you a little taste of it. So, this is actually what we call Society Library papers. So and this is just the design mock-up, we’re building it right now. But we’ve figured out a lot of different features that I’m really excited about, and we’re hopefully going to have more time to test it and make sure that it’s actually as visible as we think it is. Actually, a lot of knowledge can be compressed in a paper, and what we’re doing is actually allowing the paper itself to unpack in different dimensions. And by clicking on different snippets, each one of these are arguments. It can open up different screens where people can view the variant phrases and things like that. So we’re going to drastically simplify and compress a lot of this knowledge while maintaining all of the complexity. But making it more of like a bulleted outline that opens up and closes back up as people want to dive in deep to each one of these sections. And we want to have ways in which people can track how much of it they’ve seen, etc. There are other ways, in which, we’re thinking about visualizing content too. But that still needs to be flashed out more before I’m excited to share it. Although there are things I really think, just like we are figuring out all the ways in which we can have all the complexity and features of this debate map in a piece of paper, I think we’ll be able to do the same with video, and if some magical VR magician comes by and wants to take this data and start changing visualizations as well, I think experimenting with it in VR, is going to be very important for us as well. So I think those are all my tabs that I had an interest in showing you all. And 45 minutes in, wow, I think it’s a good time to pause and ask:

You all have been thinking about these issues longer than we have, what should we be learning? What have we missed? Who should we be learning from? Would love your feedback. And thank you all for your attention and time, too.

Q&A- https://youtu.be/Puc5vzwp8IQ?t=2030 

Frode Hegland: Yeah, thank you. That was intense, and our wonderful human transcriber is going to work overtime on your presentation. Danillo, he is very good, so it’ll be fine. I’m going to start with the worst question just to get that out of the way and that is: You’re American, we’re British. You had Trump, we have Boris Johnson. It seems a lot of that politics is just personality-based, “Oh, I like him.” Or some kind of statement like that. Where would you fit that in here? Or do you consider that, for this, out of scope?

Jamie Joyce: To a certain extent, I think there are certain things that are relevant, and some of it is out of scope. So, one thing I just want to acknowledge is that I think we’re in the middle of an epistemic movement. So there are a lot of people who are working on different dimensions of how can we have an epistemic revolution? I like to call it the e-Lightenment. So how can we use new technology to transform our relationship with information?

Frode Hegland: Hang on, e-Lightenment? Has anyone else on the call heard that expression before? That’s pretty cool. Let’s just underline that. The e-Lightenment. Okay, that’s wonderful.

Jamie Joyce: That was the name of my TED Talk, yes, I called the e-Lightenment.

Frode Hegland: Oh, no. You caught us not having seen your TED Talk, now we have to watch it. Okay, fair enough. 

Jamie Joyce: No, no. It’s old. It’s not amazing. I really wanna redo it. If I redo it, I’m gonna call it something like Big Data Democracy, and talk about the complexity, volume, and density of our social deliberations, and how we need new tools to really experience our big data society in the way in which it actually exists in reality. But anyway, yeah, so I think some things are relevant, some things are out of scope. There are a lot of people who are working on different dimensions of these issues. And one of the contributions that The Society Library is making outside of knowledge projects is also education. We’ve been working at the university level, we’re trying to bring it down to high school, and then we’ve been collaborating with some people ideating how we can start an epistemic appreciation, learning about cognitive biases, logical fallacies, and the various ways in which you can be disinformed on the internet into a younger and younger children’s programming to develop literacy, and a standard for what we should appreciate, in terms of, high-quality work versus not high-quality work. And obviously, there’s people who are working on making social media less effective and less addictive. There’s lots of different people who are working on this. And you’re right, I think a lot of politics is about personality. So what I think about often is, “Okay, well. How do we, essentially, make smart really sexy in the United States?” So once we have these knowledge products, you have to create the demand for people to want to use them. So, what kind of people need to associate with these knowledge products? 

(IN CHAT) From Peter Wasilko: Do you flag logical fallacies in the presented text?

Yes, we do. So I didn’t mention it. Thank you for your question, Peter. We have a tagging feature and we use our tags and it actually appears on the paper. So when someone unpacks a node in the paper and we have a tag on it, it appears as a handwritten note off to the side in the marginalia that just lets people know like, Hey, this is an opinion. This needs to be checked. This is cherry pick data, et cetera.” Sorry, I saw your note and just wanted to answer that really quickly. So how do we create the personalities? You know, fictional or not? These could be in kids’ shows, for example. How do we create the personality and personas that are sexy and attractive that are pointing people towards the cultural values of appreciating more rigorous research higher standard for argumentation and these sorts of things? And some of our donors and supporters have been thinking about this also. And thinking about supporting subsequent and related projects to help drive up demand for people appreciating this. And something that we see in the Trump era in the United States is that there’s been a huge decrease in trust. I think this was happening well before Trump, I think Trump was a consequence of this happening. But I think he also helped make it a little bit worse. There’s been mass amounts of distrust in existing knowledge institutions. Like news media, universities, government agencies, these sorts of things. Some sections of the population are not as trusting to get their information from those institutions. However, I think libraries, very interestingly, have maintained their level of trust in American society. So we do recognize that there is an element of branding and storytelling to be attractive to the community. It’s probably gonna be very long-form relationship development. And that’s one of the reasons why the Society Library takes its culture so seriously. We take our virtues and values so seriously because we are going to be an institution that isn’t going to get thrown away immediately. That means we have to always have the out-facing communication, the branding, look, and the integrity to earn and maintain that trust.

Frode Hegland: On that issue, on the fake news. This is a phenomenal guide to fake news, as opposed to propaganda. It basically makes this simple obvious statement that, when Russia first invaded Ukraine, the point of fake news was not wrong news. It was simply wrong and true mixed, so no one would believe the media. And, of course, clever people like to think, “Oh I don’t trust the BBC.” And, you know, the situation we’re in today, which is pretty awful. And then I have a very specific semi-technical question, this goes back to having conversations with Marc-Antoine, of course, and that is, the last thing you showed, that normal document where you can click and things open, that is, of course, phenomenal, and it is something that, we in this community, we really like the idea of being able to get a summary and then digging into it. So my question to you is: 

In what way is it open and interconnected? Can I use it in my academic document? Can Fabien use it in a VR environment? And can Marc-Antoine, I guess you can, extract it into his knowledge graph? How does this data move around?

Jamie Joyce: Well, good question. So all the data coming from the debate map can be referenced and extricated elsewhere. The paper document is so brand spanking new, we haven’t even thought about integrating it with other platforms. So we’re still wrapping it up as we speak. When it’s finished though, I would love to start inquiring into, how it can be, not only maybe productized so other people could use it, essentially, it would require a different interface to input data. Most likely because I can’t imagine people are going to quickly get up to speed with our really complex debate map. So creating a user-facing product input form into a structure that will probably be more helpful to others. So I don’t have an answer to your question yet. I would love for it to be productized and for it to be ported elsewhere. But the debate map does, that data can be extracted and referenced and all of that through an API.

Frode Hegland: Marc-Antoine, do you have anything to add to that? 

Marc-Antoine Parent: We certainly both believe in the value of making these new ways of expressing information, both in continuous text, in graphs, and making them interrelated. How interrelated, there’s many models. And I think we’re both, separately and together, exploring ways to do these interrelations. Certainly, the ability to tag concepts or arguments in text, I doubt very much that it won’t be connected to a graph realization. In that way, if you have an export from the graph, the question is:

Can you identify these things in the text document, right? And then we can speak about offline annotation. We can speak about edition. We can speak about… Somebody mentioned stretch text in the notes, yes, I believe in that. I believe in side-by-side views, personally. These are having the graph with the text coordinated, that’s something I’m pursuing. As I said, I’m not part of that team. I don’t know how Jamie’s doing that part. I am helping her more with the extraction a bit, so, yeah.

Jamie Joyce: Very much so. Well, you can join the team, Marc-Antoine. I’d love to get your thoughts. We’ve just been so swamped in the world of design, I didn’t even think about tapping on your shoulder. But I always love working with you.

Frode Hegland- https://youtu.be/Puc5vzwp8IQ?t=2560

Frode Hegland: Yes, you two, yes. So, okay. I’ll do something controversial, then, and show you something. Just briefly. most of the people here know this all too well. This is the most poverty-stricken thing I could possibly show you. But it’s about an approach, not a specific thing. I’ll do really briefly. At the beginning of documents in a book, you normally have a bit of metadata. PDFs, of course, normally never have anything. So this approach that we call Visual-Meta, is to take metadata on the last page, right? This obviously wasn’t made for you, so I’ll just show you a few brief things and mention the relevance. It is formatted to look like BibTeX, and that just means it looks this is this, this is this, really, really simple, right? So this example here happens to be for the ACM Hypertext Conference last year and this year. But the idea is that, all we do, when we export to PDF, is to write at the back of the document what the metadata is. And that includes, first of all, who wrote it, because very often, an academic article, when you download it, you don’t even know the date that was published, because it’s from a specific bit of the journal. It also includes structural metadata, i.e. headings. They can also include who wrote the headings and what levels. And then they can include references. So all of this is in the metadata that we then take into VR, or wherever, and use it. So one thing you might consider, and this is something we’d love to work with you, but if you do it entirely by yourself, that’s fine. All this stuff you have, when you do that top-level presentation, just stick it in the appendix. As long as you explain in the beginning what it is, in normal human language, let’s say, in 500 years when someone comes across the PDF and everything else is dead, they can reconstruct it.

Jamie Joyce: Yep. I checked out some of it, I think one of your explainer videos on it. I just got to say, I absolutely love it. I love it.

Frode Hegland: I’m glad you do. And thank you. I mean, every couple of months there’s a circle of arguments of, “Oh, we shouldn’t use PDF.” We don’t just use PDF. It depends where you’re doing stuff. When we go into VR, we use different formats. But at the end of the day, you’ve got to archive something. And that’s why it’s used by billions of documents. Somebody will keep it going. So when it comes to the finish bit, yeah, you know that whole workflow. I’m just glad we had that little back and forth. Any other questions? And by the way, Daveed and Karl, it looks like you’re wearing the same hat. It’s so funny. Because of the green background.

Daveed Benjamin- https://youtu.be/Puc5vzwp8IQ?t=2734

Daveed Benjamin: Oh, that’s funny. Yes. Nice. I have a question, Jamie. 

Where are you headed with the visualization on screens?

Jamie Joyce: We’re working on creating this multi-dimensional explorable and interactive piece of paper. And then, I think we’re going to move on to recreating the newspaper and recreating TV as well. Because again, all of those nodes can be the ones that have video content because we clip it where the expression of the claim is associated in video with the node itself. So, as especially we get more and more sophisticated with automating some of our processes, and making sure each one of the nodes are actually multimedia, I think the same way of compressing and compounding into a dense layered interactive set can be translated across medias. So I’m really excited about that. But again, I can’t not state enough how interested we would be in creating a VR library, because I think that would be so exciting. Or a VR debate. I think that’s really important. So we’re just tinkering right now. And we’re just finishing up the last really complex argumentation structure and creating corresponding paper features and visualizations for that. And we’re looking to push it out by the end of this month. And then we’ll test and get feedback and see how useful it is and all of that.

Daveed Benjamin: That’s super cool. I look forward to seeing that.

Frode Hegland: Yeah, that’s really wonderful. I see Fabien has his hand up, which is great because I was about to call on him. I was just going to say two things in context for Fabien. Number one, we’re doing some basic murals in VR now. And even just a flat mural is really powerful. And we’re looking at all kinds of interactions. But also, we do meet every Monday and Friday. All of you should feel free to dip in and out as you have time, because right now we’re at the stage where we’re learning how to do folding, or this, and that. So we’re at the detail level which could be really quite exciting. Fabien, please.

Fabien Benetou- https://youtu.be/Puc5vzwp8IQ?t=2861

Fabien Benetou: Thank you. And thanks for the presentation. What I wanted to say, I have a presentation due for this group named “What if a librarian could move the walls.” I think it should pick your interest. But I’ll give a little spoiler for this presentation, which is to say, in my opinion, even if, for example, your information or your data structure is very well organized, it might not be the most interesting for participants, because that might become a little bit boring. If it’s too structured, let’s say, if you go to a hospital or a large public building, if every floor is a copy-paste of the other, we get lost, basically. So being structured is extremely powerful. And we can process and we can do quite a bit with it. But I don’t think it’s sufficient. It’s not a criticism in any form or way. I’m just saying, today if you give me the data set, I can definitely make an infinite corridor, a very long corridor, with all the information. But, yes. I think it would be fun to do, but I think it’s not sufficient. I think you will experience it, have a form of way to be through it, but one of the, at least my motivation for VR porting of text, documents, or information, is how smart our body is, and how we can remember when we’ve been to? Like I was mentioning a bakery in Berkeley, because I haven’t been for a while, but I did go and I remember how to get there, and how to go in the bathroom of a friend. This mind-blowing stuff that any of us, every one of us can do. But because we have some richness of the environmental diversity, So it’s a bit of a word of warning to say, porting it to VR it’s definitely feasible today. It’s not a problem. It would definitely be valuable and interesting. But it would probably be quite interesting or more valuable to consider what 3D assets you do have. Is there actually a structure behind, let’s say, an argument map? Can you actually visualize it, not to visualize it, but in a way that you mentioned knowledge is compression that you can synthesize in a way that is meaningful. Not just to specialize it in order to specialize it, but to specialize it because that mapping or that visualization makes sense. So I think that’s a little bit of a challenge there. And again, I say this candidly or naively, I don’t have sadly an obvious or immediate answer to this. But in my opinion, that’s where the challenge would be.

Jamie Joyce: Yeah, I absolutely completely agree. And when you mentioned how intelligent our bodies are, what immediately came to mind is how, when we were looking at different visualizations for the data on, essentially, a 2D screen, where we can’t interact with it in a three-dimensional space, so to speak, I was trying to find a lot of inspiration from video games. So, I was looking at a lot of video games, of how they compress knowledge and organize it. They have all their accessories, and this is how they upgrade their armour, and blah, blah, blah. So I was looking at tons of those. And what I was really interested in and inspired by, were these things called star charts. So there are ways in which people can develop their character, where they move through a three-dimensional space of lighting up nodes and it essentially shows that they’re headed in a specific direction, it shows them enough of what’s ahead of them, so they know to move in a direction, Final Fantasy does this, for example. And they also know there’s a whole other section over here of undeveloped traits because their character is moving in a specific direction. And so when I was thinking about our argument maps, if we were to take those trees, and essentially lay that out in physical space, is there some kind of metaphorical thing that we can pull from real life that would map onto people’s brains really easily so that they could use their geospatial intelligence to, not only remember the content but be interacting with a little bit more? And I couldn’t help but think like, essentially roads. So all of these different signs that would indicate if you want to go to economic here, blah, blah, here, blah, blah, blah, here. And they could be taking a walk through the debate. And it could be an enjoyable experience because there could be lots of delightful things along the way that they could be seeing. As they’re walking in the direction of the economic arguments there’s another signpost with all the different signs pointing in different directions, they take this one, etc. There’s the map up on the corner showing them what territory they’ve explored. This is something that exists in video games. And I mean, given the structure of our data in debate map, it seems as though so long is similar to how we are designing all of the assets in the paper to unlock, unfold, split up, and blah, blah, blah. If those similar assets could be rendered in a 3D space, you could just map out entire territories of physical traversable space. So I don’t know, given that you’re an expert, if you think an idea like that could be worthwhile. But I completely agree with you that just visualizing it as books or something on a shelf isn’t gonna do it, because we’re a different kind of society now. And so, part of the reason why we conceptualize this idea of a web-based conceptual portmanteau is that we know that we have to develop new media to express knowledge. We can’t just directly digitize books, or pages, or essays, or newspapers anymore. It’s multi-dimensional.

Frode Hegland: But there is a really interesting “however” which we experienced recently. Bob Horn gave us a mural that we put in VR. And if you pull it towards you and push it, no problem. But if you walk it’s so easy to get queasy. Motion sickness, for so many of us, can happen. So the idea of walking down a road it’s great for some, but it can easily just not go. But also, before I hand it over to Mark here, Brandel forced me to buy this book last time by holding it up, that’s how we force each other here. And the introduction is not very good. It really threw me off, because she’s a journalist. However, when you get to the chapters, the embodied thinking and so on is absolutely phenomenal. I think you would greatly appreciate it. But, yeah. The reason I highlighted that point is me having been in VR only three months now. Properly on and off. I had so many preconceptions that are just getting slaughtered. So to work with Fabien, with such rich deep experience, and also with Brandel, who’s working on these things to really learn to see differently, we have to re-evaluate this. For a while, we call what we’re working on, Metaverse. But just looking at the proper definition, that’s all about the social space. What we’re doing in this group is not so much social and definitely not gaming as such. It is about working in virtual environments. And it seems hardly anybody’s focused on that. So for you to come into this dialogue with actual data, actual use case, actual needs, that’s a really wonderful question. So, I want to thank you very much.

Jamie Joyce: Yeah, and thank you for that advice. And thank you, Fabien, too. So, are there meetings that maybe I could sit on so I could benefit from this sage insight and experience of translating things to VR?

Fabien Benetou: Sorry to interrupt, but to be really direct, did you put up your headset for the last couple of years?

Jamie Joyce: It’s probably been about a year since I’ve put one on my face. 

Fabien Benetou: Okay, but you did. So that’s fine. Because, I think, honestly, with all the due respect to everybody around here. Putting their heads at once recently is more valuable than any or all of our meetings. And then coming in, discussing, and then, proposing a data set is definitely valuable. Yeah, but that’s the first step. You’ve done that part, you’ll get it.

Jamie Joyce: Yeah, I’ve played some games. I love the painting games a lot, actually. That’s pretty fun.

Frode Hegland: Very good. Just to answer your question about sit in. You’re all invited to just be in any of the meetings. It is the same time. It should be four to six UK time, but you Americans move the whole clock. So we follow you. Right now we will catch up with you. But sit in, say nothing, speak, whatever you feel like. It’s just a warm community. Mr, sorry, professor, not professor, Dr. Anderson.

Dr. Anderson- https://youtu.be/Puc5vzwp8IQ?t=3364

Mark Anderson: Okay. Well, thanks. I really enjoyed the presentation. And I’d love to hear someone just mentioning the idea of a data set because given the deconstruction you’re doing, there’s something really interesting there. At the moment, when people talk about data set, that just means something I scraped out of an excel spreadsheet and shoved in a box and I now think it’s worth money. Fundamentally not what I think data is. And it’s really interesting too for this thought of VR, one of the things, so I’m kind of completely new, I suppose, two months, I guess, since I’ve looked at any certainly any mode in VR, and I’ve been using Oculus. And then, one of the things I’m really seeing is that lots of things that you’d think would work, just won’t. So take pretty much any 2D print visualization you’ve thought of, it’s not going to be instantly better for seeing it with an extra D. That’s for certain. Which is, in effect, why the data is so much more interesting. So rather than think, “How do I make this picture, this 2D picture appear in 3D?” With all this richness, how can I see things that I can’t (indistinct)? Anyway, probably something that preached the converted there. Just a couple of thoughts but against you rather cheeky and I feel bad about it because they’re only possible to make given the mass amount of work you’ve done and the wonderful deconstruction of the arguments. But I suppose the hypertext researcher are used to looking at non-linear paths and I was thinking, does your deconstruction process show you where the same sources or the same arguments occur in multiple parts in the graph? Because I think that becomes useful. And also, the whole Johari window problem of the unknown unknowns. There is a danger and we’re all prone to it, is that, because it takes time to do this, by the time you’ve mapped everything out, that seems like the known world. And I can’t see how to still force myself to say, “Ah, that’s just the bit I know. Now let’s look at the broader thing.” I don’t know if there’s an answer to that, but it’s an interesting challenge. And the one other thing I was thinking about prompted by the thought of changing views about how we trust data, sources, and things is, we seem to have moved into a world where there are massive first-mover advantages in being the first to complain, for instance. So there’s moral ascendancy being the first person to call the other person bad, regardless of the truth or situation. And it also tends I’ve been a thing creeping away if there are two pros and one con or vice versa, that’s seen as actually being an empirical measure of work. How do you cope without or does the deconstruction model not attempt that? Because I’m not saying it should, but what are your feelings on that side of things?

Jamie Joyce: Yes, okay. So there are a couple of different things that you said and I’m going to try to remember and respond to them all. So one: Can the system understand that the sources are the same? Yes. But there’s no features built on top of that to make that easy. And it’s deceptively linear because we can actually copy and paste nodes all across and it does link all around it. And if we update one, it updates the other, etc. So it’s deceptively linear. But definitely, it could be more rich and useful as a knowledge graph if we build features to actually filter content like that. We don’t currently do that. The other thing that you mentioned is about knowns and unknowns. Actually, we’ve had extensive conversations at the Society Library about this very thing, because we have this technique called devil’s advocacy research. It’s something we borrowed from the CIA. So, if you take a claim and you just invert it to its opposite, and then, you go and try and stillman that, that’s a CIA technique at least as recent as 2009. And so, because we generate so many claims, I think our climate change database is like 396 thousand claims of a single expression, not variant phrases but single expressions. We could just invert all of those and have a whole other set. And then there’s, of course, you don’t just invert things in a binary sense, there’s all these shades of grey in between, there’s all these adjectives that you could add that slightly change the meaning of a claim. So there’s a lot that we could do there to, essentially, once we have the set that we have, the known, knowns, to invert, slightly adjust to drastically expand that. And then what we could show as visualization and we thought about this, would it be a useful epistemic tool to show people? This is what we were able to stillman, but these are all the different research questions we have, that we were just able to generate that are relevant to a certain extent. It’s not just nonsense created by GPT-3, right? It could be relevant and we haven’t done the work yet. And we didn’t know if that would be that would increase intellectual humility and curiosity, or that would be really disincentivizing and discouraging. So it’s beyond our organizational capacity to that experiment. But we have been thinking about and are interested. When it comes to trust first movers, I see people coming into this space and being the first movers of complainers and I’m seeing them rise in popularity and it’s very interesting to watch. But I can’t remember why you brought that up.

Mark Anderson: Well, it bleeds into this point about people getting overly empirical. So I’ve seen two supporting things. So clearly that’s more than one countervailing argument. Not just the user, the learner from this not actually learning, they actually have to evaluate. Having got to these sources and actually having to evaluate them. It’s not so much just counting up pros and cons.

Jamie Joyce: Yes. I’m going to quickly see if I can pull this up really fast. So we’ve been thinking about that also. And one of the reasons why we have tags is to start qualifying things so we’ll call out if an argument has no evidence since it’s just an opinion or logical fallacy, etc. Because we’re trying to combat some of those cognitive biases. And so one of the things that we want to do as clunky of an idea as this is, we do have an intro video where we’re going to try and prime people to not fall for these different cognitive biases. To tell them explicitly do not fall for this trick. Having more does not mean this is better argumentation or what have you, do not fall for this trick. We’re thinking about making it so that you can’t even unlock the paper map decision or library until you play a video that helps inoculate against that. And something that I’ve been just personally wondering is, can cognitive biases cancel each other out? So if people are one: more likely to remember the first thing they read, but they’re also more likely to remember things that are negative, should we always show the con positions or the no positions at the end? And should we, in these intro videos, tell them that there’s no way that we can get around some of these biases? Because they’re hardwired in our brains. We’re just primed for them. So we’ve organized things in this way. If you think it’s biased, it’s because it is, but we’re trying to counteract this other bias. So we’re trying to find that, is there a communication medium whether asking them to watch a video or have a little character pop up, a little tiny robot librarian bloop up and be like, “Hey, just so you know, we did this for this reason because humans are biased and flawed and we’re just really trying to get you to enlightenment here.” We’re thinking about it. We have no great answer. And we do have a partner at Harvard and NYU who offered to run a polarization study to see if the way that we map content can depolarize attitude. So we are interested in partnering with universities to really rigorously test some of the features that we’re thinking about, just to see if it does have a pro-social positive impact because we’re not interested in persuading anyone. We’re not interested in driving anyone towards any conclusions. We just have the librarian goal of enabling enlightenment through access to information. And for us, enlightenment means potentially open mind through depolarized attitudes, inoculation against disinformation, intellectual humility, increased subject matter knowledge and increased comprehension of complexity. So just overall more curiosity, open-mindedness, and comprehension without being inflicted by bad attitudes, depolarization, disinformation, and things like that.

Mark Anderson: Well. that’s lovely to hear, actually. And I’m thinking, of course, that again, the joy of you having such a deep and rich data set is, for instance, although, you can’t necessarily answer some of the stuff on these biases, there’s the lovely substrate for someone to work on. I mean this is again where I think people fail to see where the real value in the data is. It’s not like you’re going to sell this to somebody. It’s the fact that that’s just hours of dedicated work. And especially doing it from, in a sense a neutral, for a lack of a better word, but a standpoint which (indistinct) important because you rightly stay. I mean if you’ve got some bias in there or if you’ve got more than a trivial amount of bias in there to start with, then (indistinct). And just because I see hands up, but one final thought is, when you mention the fact, yes, inputs to turn up across the piece. That, for instance, might be an area where having extra dimensions visualization might be exploitable because it’s really hard to do on a flat surface because the worst thing is all you said end up with lines all over the place, and it’s alternatively complicated. But I think that one of the things that are submerging in our exploration of what VR is its ability to, you don’t necessarily have to remove things, it’s reducing the salience of some things. Bringing it, dialling it upon others. So it’s all there. It’s all somewhere in the space, perhaps. But what you’re seeing is the connection that’s pertinent at the time. It’s a different sort of interaction. You will ask for the thing you’re interested in knowing and bring it forward. Of course, thinking that and making that up is the journey we’re on at the moment. But, thanks. I find that really interesting. 

Jamie Joyce: Thanks for your questions, Mark.

Daveed Benjamin- https://youtu.be/Puc5vzwp8IQ?t=3965

Daveed Benjamin: Excellent. Hey, Jamie. So my question is, well, the premise of it, is that what you’re producing is going to be incredibly valuable, I’m just making that assumption. And I’m also looking at it and just seeing it, seems like there’s just such a tremendous amount of work that goes into just one inquiry. And what I’m wondering is: What does it take right now, for example, to do something like the California Nuclear Plant both in kind of human resources, as well as elapsed time? And then I’m also wondering what do you foresee in the future, in terms of being able to streamline that with both automation and potentially AI? What do you what are you shooting for, in terms of human resources and elapsed time? And then, the third part of that is: 

Is decentralization, at all, on your radar in that possibility of bringing in a much larger group of analysts to do certain pieces of the work?

Jamie Joyce: Yep. Great set of questions. So I believe the Diablo Canyon Power Plant project is about 10 weeks old. We got two more weeks left to wrap it all up. And that was inventing the paper visualization along the way. We had four full-time analysts. I was a part-time analyst. So it’s kind of a small team in a relatively quick period of time and I think we owe a lot of that to the tools we built ahead of time in the past and our methodology. And the fact that we have years of experience training students through our educational curriculum, so we know how to train people to like quickly understand debate map, quickly understand what we might claim, use the tools to find content, but I honestly think, a lot of the different tasks, not the work, I think we’re going to be working with librarians and human analysts for a long time, but I think a lot of the tasks, discrete tasks, can be automated. We’re tinkering with some of those right now. I’m fundraising for some of that right now. I’ve got a lot of ideas about what’s possible in both like claim mining, syllogism generation, mass deconstruction,  there’s a whole bunch of ideas that I have. And there’s already tools that exist that we could be experimenting with more. So I’m excited about that. You mentioned decentralization, I think there’s a question between that. With decentralization, the thing is that, language is so flexible and dense, and some people are not very precise in their expression. So it depends upon the knowledge that you are working with, first of all, because we’re working across different media types, there’s a lot of flexibility in that language, there’s a lot of ways in which people can misinterpret, they can imply, they can bias the interpretation. So if we were to welcome more of a crowd, there would be discreet tasks that I would allocate to them. But I would not trust a crowd to be responsible for the emerging structuring of a deliberation. And that’s because, unless this entire crowd is somehow really well trained in understanding what is relevant argumentation and what is not relevant argumentation, you’re going to end up with a humongous spaghettified mess. If you look at existing platforms, I’ve looked at a lot of platforms, if you look at existing platforms, you’ll notice that the argumentation is either very vague enough, where a lot of the relevance can be applied. Or it’s not really fine enough in terms of actually establishing the logical relationship between the points even if the points are more specific. So it’s not to the level of rigor that we’re interested in the Society Library. And that’s because like the knowledge project products that we’re looking to create, even though we’re creating the options for people to simplify things. Simplify this and put it into simple variant phrasing, for example. Just give me the gist of it. We want to give people that option. We actually want to do, as rigorous work as we possibly can, in terms of deconstructing arguments into their processes and conclusions. Because if I feel like if you don’t do that then it’s always going to yield more and more argumentation because people will misunderstand what’s implied. So, if you actually pull apart the argument like, “This is every single stage of what we mean. This is all the data that supports those things.” Maybe it allows some tiny small subsection of readers to really appreciate that more. A lot of people are not going to want that level of detail. Just give me the gist so I can see and make my decision. So there are certain things I think the crowd could do really well. I think archiving is something the crowd could do really well. I think tagging is something the crowd could do really well. Modelling argumentation I think that’s a really high skilled skill. I think that’s a really technical skill and I wouldn’t trust like a hundred thousand people to do that in a meaningful way. I already get in wiki wars on Wikipedia, for example, and that’s just an encyclopaedia page and there are no rigorous rules about the relationship between sentences and Wikipedia. And yet, people still fight about that. So yeah, that’s my point on that. 

(IN CHAT) From Marc-Antoine Parent: That does not mean that it cannot be partly crowdsourced in principle, but certainly not naively

Yeah, partly crowdsourced in principle. That’s right. I agree. There are parts of it that could be crowdsourced like finding the topics, getting the resources, finding how topics, and base arguments appear in certain resources. So again, archiving and tagging, I think it was a great crowd job. But modelling, I think requires a lot of skill and a lot of editorial review. I review the work of all of the analysts. We review each other’s work. In the future I want us to have more of an inner coded system, where a lot of the work is actually redundantly performed, the same people performing the exact same task so we can actually see the difference, and see if that difference is statistically significant. There are people who build distributed content analysis platforms that I really like. They’re friends of ours, they collaborate with us on certain things. I’m not yet finished with fine-tuning our method enough to know what we want to have as a part of distributed content analysis and what can be automated. So maybe a few moderations down we’ll have the right combination of like, “Okay, we’re going to hard code this modelling into something that is distributed,” and then also have AI help us with certain discrete tasks, and maybe a crowd. We’ve been poked and provoked to do a DAO, as well. So, I don’t know if we will, but.

Daveed Benjamin: The question that that was in between was actually related to the first question. In the best of all worlds, where do you see the elapsed time getting to…  Because, especially, when we’re talking about a culture with this first complaint dynamic happening. It’s like getting this information out quickly, I think could be really valuable.

Jamie Joyce: Yeah I like to think and try to orient our work towards constantly imagining it being possible instantaneously. There being constant monitoring, construction, and modelling that’s happening, I think we’re really far away from that. But that’s what I would like to get to. Essentially, the Society Library, being a large enough institute to have the manpower to respond where we need human analysts intervening, and also the technology to be observing, deconstructing, labelling, and doing base categorizations. De-duplicating these sorts of things. Finding the right combination, a lot of the unloaded work is being done by AI, and we have enough staff, librarians, essentially, serving society quickly, modelling up this content, where just absolute elite experts, and then, having all the tools that they need, in order to quickly do that. So, journalists are reporting and stuff is happening on TV we can be quick on incorporating that into higher dimensions and more complex mapping, epidemic mapping of a situation. So I’m hoping one day, I don’t know if it’ll be in my lifetime, I’m probably underestimating technology, but I don’t always imagine that it’s in my lifetime, but I’m angling towards us having an instantaneous institution for this. At least for publicly accessible knowledge.

Frode Hegland: I see Fabien has his hand up, but I just want to say thank you for something very specific. And that is surfing the line between being popularist and being arrogant, or elitist. Because I do think it is really important to still value expertise, and our current culture isn’t so happy with that. I’m sure you have seen Hamilton. I’m sure you remember Aaron Burr, I could have a beer with him, right? This is horrendous damage that is being dealt to us. So by you standing up for expertise without being arrogant, without taking a position, that’s just fantastic. So, thank you for that. And, Fabien?

Fabien Benetou- https://youtu.be/Puc5vzwp8IQ?t=4486

Fabien Benetou: Yeah, my question is, and maybe I missed it, but how do you interact with the result? Or how does somebody who wants to learn, let’s say, get the expertise out of a topic, gets that? What I saw through the presentation, and again, maybe I misunderstood, but was unfolding the different part of the map. But is there another way to interact? I’m asking this specifically to see also again how could this eventually be coded or considered for VR? Because a visualization, of course, is an object and you can see it but you can do more with it. You can, for example, fold and unfold, but once you got, again for VR, their controllers, or even your hands, you can manipulate it. Or if it’s text, you can copy-paste it. So they are very different and rich interactions. So, yeah. I’m wondering what’s the styles of interactions right now? And what was the thinking process behind it? Because more interaction doesn’t necessarily mean better. You want to concentrate on something productive. So, yeah. If you could dig a bit more there, I’d appreciate it.

Jamie Joyce: Yep there’s a couple of different visualizations that we currently offer. It’s essentially the same data, but we compress it and organize it differently. For some visualizations, we filter things out. One thing that we do is we do make the map available. People can unclick and expand and do all that. I don’t think a lot of people are going to find that attractive. Some people are going to love it. Two is, we’re revealing something called Society Library Papers, where all of the data in that debate map is actually structured in a piece of paper, where you can click on the lines and it opens up options to further unfold, not only the argumentation but into the note itself. You can refresh so the different phrases and different ways of expressing things show up. You can press keys that will swiftly change it from technical to simple language. So you’ll have your standard way it’s expressed, you can flip between those like, “Show me the more technical. Show me the more simple.” It unpacks the argumentation. So, Papers is the new thing that we’re rolling out. The other thing that we do is create decision-making models. So we just zero in on one sub-section of argumentation at a time, and people can add values to how they’re weighing the different arguments. And then, essentially, we ask them to micro vote. Where is the strongest argumentation in this one subset? And then they move on to the next set, and they move on to the next set. At the end, it’s shown to them like, Okay, well. Where do you stand on these issues? Economic, environmental, etc.” The decision-making model is the thing that we do at City Council. And then the other thing we do is we just make all the resources that we generated available in a tech searchable library. So you can look through all the references that have been included in this data set, you can keyword search and find the claims not necessarily associated with each other. You can search it on the map as well, so it’ll bring you to the part of the map where that claim is. And that claim can be in multiple places, so you can bounce around and see where all the places that this claim is. Or you can just search it in a library list. So those are the four things that we have, in terms of visualizing content now, but I’m excited about, thinking about doing the same, kind of, paper unpacking and unfolding, but with video. And then unless we do something specific, like write legislation. We also have a designer who’s working on a phone app version of it. In terms of being able to interact, for example, with the paper, you can click on any line and it gives you the option to expand it in various dimensions. Show it to me in a video. Show it to me in a podcast where the references that, support this, where the bearing phrase is, etc. It would be so cool if in VR, and again, I don’t know if this would actually translate well. It’d be cool if you could take a statement and actually open it up. Grab that statement, open it up, “Oh, I can see all the videos, TV clips, where this occurred.” Swipe to the left, okay, here are the references. Okay, definitions. Okay, media artifacts. Okay, close that one up, star that, want to look at that later, grab the next one, let’s open that up and take a look at what that looks like, or do a motion like this, and it just spills out all the different claims that support it. I think there could be a lot of cool interactive ways that could make text a little less boring, simply because you’re moving your body. And moving your body may create endorphins, and make you a little bit more happy and excited about stuff. And that’s what we’re trying to do with Papers. With Paper, I didn’t show you any interactivity at all, I just showed you the mock-up. But we’re really focused on how it feels. The slickness of the unpacking. The slight little sounds. We’ve been looking at the colours when rendered to accommodate for different like visual, I don’t want to say impairments, but different visual differences. And it looks gorgeous, all of them, in my opinion. So we’ve been really focusing on the feel of it. Because we know it’s limited by text, but if we could translate that to VR, I think, it could be much more interesting just by being able to literally work with knowledge. Grab knowledge, put that over there, unpack, all that stuff.

Fabien Benetou: A quick remark on this, I don’t know if you’re familiar with the interactive explorations. Basically it’s the idea that you can have exercises in the middle of a piece. You don’t just have a piece of text but, you have an exercise. And it’s not just a textbook exercise, but it’s part of a story. So that you, in order to get the core idea of this article or concept or paper, you go through an exercise. So it’s a guided interaction, basically. You’re not just freely moving things around, but you’re solving a small challenge that’s going to get you to this eureka moment that the author of the paper had at some point and that’s why they were sharing this some kind of information. And I think that, also, is something that could be valuable. Of course, freely manipulating, but a guided manipulation that makes the person, who has to interact with, get the point of it. It could also be quite interesting, I think. Here, I don’t know how literal or metaphorical you want to be. Let’s say, you display a nuclear power plant. How close you are. You can zoom in and out to the atomic level or not. Yeah, there is a lot that can be done there. 

Jamie Joyce: Oh, sorry. I didn’t mean to interrupt you if you saw my hand waving, because I got really excited. That’s good because we did a mock-up once. We didn’t get enough money for this project. But we actually did that for our Covid collection. So we built a 3D lab. We just did this in Prezi so it was a very superficial mock-up. We did it in Prezi because it has that zooming in and out feature. And we built a 3D lab where you could essentially zoom into the lungs of the lab worker who was there and explore all the different argumentation about respiratory health, and what Covid does to the body. You could zoom into the microscope to learn about argumentation about what SARS-COV2 as a virus is, what are its features, what are the pictures that have been grabbed, what kind of telescope, etc. We did that, where we built a scene and people could zoom into different dimensions of it, to explore different topics, sub-topics of debate within that. But we didn’t get enough funding to really do for real. But I think that would be really fun.

Fabien Benetou: The funding part, to be honest, it’s about to lack in the sense that, it’s super demanding to get this kind of materials. Designing a 3D experience. But I think for those two cases, nuclear or Covid, they are excellent for it, because there are skills that are not graspable for most of us. And I think for a technical expert then it becomes natural because you did the exercise so many times that it does become natural. But being able to change scales, in a way that still makes the intangible, tangible, it’s a perfect use case for this.

Jamie Joyce: Amazing. I’m excited to sit on all these meetings.

Frode Hegland: Peter are you is that an earlier hand or is that a fresh hand?

Peter Wasilko- https://youtu.be/Puc5vzwp8IQ?t=5019

Peter Wasilko: It’s an ongoing hand that’s been having things added to the queue. Your description of waving your hands around reminds me a little bit of the user interface depicted minority report. That was a very interesting movie to take a look at for the VR-type visualization. Well, more AR-style visualizations in it. Also the talk about instant votes and things remind me of a wonderful episode of the Orville called Majority Rule where everyone would walk around with smart badges that had an up arrow and a down arrow. And if you accumulated too many down arrow votes, you’ll be lobotomized by the society. So, just fascinating. Then substantively, have you taken a look at the foresight literature? There’s a concept called Futures Cone, and I put a link for that in the chat. The basic idea is to represent multiple possible futures. And it seems like that could be a good visualization for providing an organizational access layer to the dialogue, because some of the different points would correspond to the same possible future. And that could provide a different view on the evolving debate structure. So, for instance, there would be one possible future where the claims that the climate temperature is going to go out of control is correct. Then there’s another possible future where it turns out that those studies might not necessarily have been accurate, more an artifact of modelling software. So you could take those possible futures and represent them in a visualization, and use that as a filter onto the debate structure. Also, another very interesting diagramming technique is the use of state machines. Very popular in computer science. And it’s been touched on in linguistics and parsing to some extent. And the notion there is that you have a series of states, with transitions between states, and you could associate different arguments with transitions between states, where the states could represent possible futures in the futures cone visualization. Also, I would suggest having a look at the system dynamics literature, which has its own suite of visualizations. Some of which are very nice web-based tools that look at feedback loops between different stocks and flows. And that again could provide a filter into the diagram structure as some of the diagrams would relate to different elements in that visualization. So those are a couple of possible filtering access layers that you might want to have a look at. I have all the links to the side chat.

Jamie Joyce: Thank you so much. I think I found everything that you post in the side chat. I super appreciate that. Getting some interaction interfacing with the forecasting community has been of interest. But I haven’t had the capacity to explore what is the extent to which they model futures. Because I obviously would be interested in taking those projections, deconstructing it and seeing what it looks like, in terms of, translating it to Society Library language and concepts, so I can get a handle on it. But I haven’t had the capacity to check that out. But thank you for these links, because it’s definitely on my list.

Frode Hegland- https://youtu.be/Puc5vzwp8IQ?t=5217

Frode Hegland: Talking of links. First of all, as you know, this will be transcribed and put into the journal, both your presentation, this dialogue, and the chat log. So whoever’s interested, go and have a look at our first two issues. Mark and I are still learning how to make it navigable because there’s a lot in there. But then my question is actually quite different and that is, a few years ago I took an online quiz in a Norwegian newspaper on politics, and it asked me, what do you think of this, what do you think of that? Click through, very simple. At the end of it, turned out that I should be voting for the Christian democrats. Which was a huge surprise because, whatever. But so my question to you then is: 

Have you considered letting your users model themselves in such a way, so that when they go into this, they have a stated position that the system understands that is based on their answers? 

Jamie Joyce: I mean, when I think about those kinds of quizzes, the first thing that pops into my mind is, I really want to see the data that they’re using to suggest that. Because I want to know if their interpretation of this candidate’s position is the same as the language I would use to describe my position, interpreted by the language that they’re giving me, maybe a multi-choice format to express it. So I personally have trust issues with those particular things. There are so many different possibilities to create products from these data sets.

Frode Hegland: I’m thinking not necessarily about the quiz, because, yes, that may just have been a journalistic gimmick. I’m thinking more about coming in and let’s say, most of us here, we go and say environment concern, high. We state where we are on specific issues. Health care should be shared or not? Just a few yes or no. Because that’ll put you because you talked about a constellation earlier, there’s also the thing, the spider graph, where you have lots of different dimensions and you can then see the shape of different people. But as long as people can be shown that dimension of themselves and say, “this is who I believe I am,” that may help their interaction, somehow. I don’t know if I’m going on a huge tangent or not.

Jamie Joyce: I mean, one thing I think is interesting is that, these types of quizzes, whether it’s Enneagram, or Myers-Briggs, or whatever, people love to conceptualize themselves, I think. These things are popular because, within a certain subset of people, they want to call themselves and identify themselves as something. Just being like, Oh, I’m INTJ. Or I’m (indistinct).” Some people really love that stuff. I think that could be a cool offering in terms of, “Here is your graph of beliefs.” Like an astrological chart. Here’s your sun sign and whatever sign. Here’s the dimensionality of your beliefs. I think that could be cool. But also, you made me remember something, which is, when we were talking earlier about trying to combat cognitive biases, and I mentioned that I believe it’s the cognitive bias that people remember the first thing that they see. Or people may have a backfire effect if they see something that immediately contradicts what they already believe. So people taking a quiz upon being introduced to a new subject, it would require for us to have an account system. They’d have to create an account, so we can remember these preferences. I think it could be, potentially, a way to combat bias if we knew what people strongly held beliefs are so that those could be expressed first, and then, they can be confirmed as being understood. Because I think that’s how you can overcome backfire, is you let people know you model to them who they are, we hear you, we understand you, and here’s the strongest version of the thing that you believe, here’s everything that you could possibly want, and now let’s go explore everything else. So I think that would be useful. Again, to start changing the way we interact with information, to enable enlightenment and open-mindedness for people who want to opt into something like that.

Frode Hegland: Yeah, I think that’s very interesting what you’re saying because a cognitive bias is not a bad thing, in and of itself. Same as prejudice. Also, is not a bad thing, in and of itself. Without them, we can’t function in the world. And, of course, who you express you are, depends on the circumstances where you are asked to show who you are. I do think it is really an important issue because, for instance, our son, beautiful four and a half-year-old, Edgar, he goes to a catholic school. But we weren’t sure about putting him in a religious system. But the reason we decided to do that was, I’d much rather argue morals with him at home, rather than him go to a school that doesn’t allow that. And then, try to teach him to be nice, because a lot of these decisions come down to how do you see your neighbours, how do you see yourself, how do you see the planet, all that stuff. Everything is filtered. You can’t argue facts is something we keep being told again, and again. So I’m just wondering, you have this incredible information landscape that is intelligently put together, if there was a mechanism of someone, maybe even stating their beliefs, and then when they go into the system finding out that they’re not actually behaving within their stated beliefs. The typical thing being a right-wing Christian. There’s no chance in hell Jesus was a right-wing Christian. As an example. To not only get the information in, but having a thing that is representing where it goes in. 

Jamie Joyce: I think in the future we’re going to have a lot more capabilities to do things like that. And I hope so because knowledge and information is so powerful and impactful, and if we could just improve that relationship or objectify knowledge and have a new type of etiquette around how we interact with it, and allowing it to change us, and open up us. And mirroring that it understands us and can, again, contrast us. I think that could be really wonderful. I think it’s far out for the level of sophistication that you’re talking about, or I’m just failing to imagine. But I hope we get there, because I think that would just be so lovely. I personally love to think of humanity as a species on an information diet. I think how we really survive is on information. All the different inputs that we have. I think making that relationship even more sophisticated is, hopefully, in our future and for the best.

Frode Hegland: Yeah, that’s wonderful. And sorry, as a just a tiny little thing, and that’s, if you read Sharon Lanier’s book on his VR experience journey, it’s very little about the environment and very much about the self. How you change yourself in the environment. So to think about this, in this context of being able to go into this information with an awareness of different ways you are yourself in this space. I know we’re talking a huge down-the-line kind of thing, but it was just interesting to hear that. Right, Mark. I will shut up for a minute.

Mark Anderson- https://youtu.be/Puc5vzwp8IQ?t=5649

Mark Anderson: Okay. Very quickly because I see all the hands are up. And just to restrict myself to just one observation. Another thing I think the interesting that comes out of the really interesting deep landscape, data landscape you’re making, is the ability to look at the, well, almost the metadata. So when you look across the problem space, where are the references coming from? So, in other words, there’s a whole scheme that goes over the top of this, which is not part of the augmentation or argument discovery, per se. That’s quite useful in an intelligent, very small eye sense, in terms of, understanding the problem environment space, I think. Anyway, I’ll leave it there because I see there are some people, and some haven’t spoken yet.

Jamie Joyce: I will just say quickly, Mark, what you may love to hear is that we do take great care to stillman things. So if we find arguments on TV or in news, we will try to see where is the rigorous academic literature on this. So it’s not just by luck that we’re identifying and associating arguments with media types, because we always try to get the most rigorous as possible, and the most accessible. So if it exists in different media sets, we’re really looking for them.

Mark Anderson: Yeah, it’s just this interesting thing that sometimes now, seem certain sorts of arguments seem to come from a… Or in a certain type, I don’t want to typify it too much because then you get into the labelling, but it’s just the sentence that, whereas you might think it’d be distributed across the piece. Going through all channels, or all age groups, or whatever. It can be quite fragmented. And that’s the kind of thing that the rich data that you’re collecting also enables you to see, I think.

Jamie Joyce: Yep, I agree. Okay, who would like to go next? Karl or Peter?

Karl Hebenstreit Jr: Yeah. I posted a link on, Peter Elbow had this article about the Believing Game and it’s some nice connection between that and the Six Thinking Hats. So it’s systematically speaking validity in what you don’t agree with. And then, it’s interesting with dialogue mapping and Jeff Conklin’s. Both Jeff and Edward de Bono, they focused so much on dialogue mapping and Six Thinking Hats being a meeting facilitation process. But then, there’s also the whole individual sense-making process too. I’m very big into dialogue and the facilitation process. How do we get people engaging in real-time. A thought I had to bring that to your attention.

Jamie Joyce: Yeah, thank you. I copied those I’ve never heard of the Believing Game or the Six Thinking Hats, actually. And I think that there’s a lot that we could learn from bridging communities and facilitation communities. Because what we’re trying to do is a technologically induce a space where people can interact with knowledge maybe as if and the different positions maybe as if they were interacting with a person. We’re not simulating that. But like what are some things, in terms of, the visualization itself that could create that container? That would make people feel receptive and that sort of thing? So I think there’s a lot that we can learn, and I try to pick up things here and there from facilitation, mediation, and bridging, to learn those things.

Marc-Antoine Parent: Just a quick thing. I mean, concluding. I’m working with Jeff Conklin right now. His work really shows the value of facilitators in de-personalizing arguments and creating these syntheses, usually in real-time. And I think it goes with what we were saying earlier about the importance of argumentation as a skill. And this was synthesis map-making and consensus making as a skill. And, yes. I agree totally. The question is how to weave individual sense-making, which is a more and more important activity, into creating these synthesis maps. And the question of creating synthesis from individual curated maps to collective curated maps is really the key articulation. But it’s not going to be just crowdsourcing, it has to be learned. And there are many paths towards learning to do that. And it has to be social learning about how to create these consensus maps.

Karl Hebenstreit Jr: Just one quick thing too with the way Jeff separated out. So you have the issue mapping, which is gaining the competency with compendium and creating the maps. And then there’s the dialogue mapping, which is the facilitation process. I think that’s really important for all these tools.

Marc-Antoine Parent: I personally believe, sorry, Jamie, we will need more than one mapping when we will need to connect them. For example, I think what Jamie is doing is a wonderful epistemic map. Why do people believe this? And someone was bringing these future maps. But when you do a future map it’s, this may lead to that, this may lead to that. It’s a totally different temporal presentation. They shouldn’t be on the same map. But you would want to know why do you believe that this may lead to that, which is the epistemic dimension. Connected to that and vice versa, right? Why is the belief that this may lead to that also feeding into the epistemic questions? They’re different representations. I don’t think there’s ever going to be one representation to them all, but we need to make a representation (indistinct).

Jamie Joyce: Speaking of communication. One of the things that I was thinking too is that I’m not a facilitator. So I have very limited knowledge of what that tails. But my understanding is, facilitation and mediation include deploying all sorts of different communication techniques to position people in the space where they can then proceed with a conversation and interact with something that’s potentially conflictual. So I’ve been thinking too is, maybe there could be, in thinking of how do we borrow from facilitation to enable interaction with our content in a successful way, it may there be a (indistinct) feature where we turn it on different facilitation communication strategies. So someone’s interacting with knowledge. And someone’s going off track, they know how to like, “Oh, hey. Okay. Let me reframe what you just said, and let’s move it now over back to the map.” Do there’d be a relationship between a chat bot that could carry on the conversational AI element, to walk people through the epistemic map, because it may be too dry to ask someone to go back and forth, pro and con, down from a position to more precise argumentation. That may be too much. Besides readers who are just generally interested in exploring a deliberation. But in deploying it to a chat bot, integrating it with that, I think is something that could be in the future, as well. And if someone isn’t already working on capturing all of the facilitation techniques, I hope they do. And they train an AI to be having that, would be cool.

Frode Hegland: Yeah. You’re talking about something being too cold. I think that’s a very good question here. You’re building an incredible intellectual tool and of course, emotions will come into it at some point. So you’re enthusiastic for VR and I think you understand VR like we do. Just multi-dimensional. Doesn’t matter if it’s a headset, or whatever particular. Will definitely come in to make people feel more embodied, more involved, and more aware. One of the great things about this book I keep holding up is, it points out that, if you want to be more rational, you listen to your body more than your head. Your head is more emotional than your body. Which was a bit of a surprise. So, we can help people get those understandings. I’m not sure why Brandel is not here. He’s always available. He’s very involved, so there must be a good reason. So when he watches the video of it at some point, we miss you, Brandel. I hope to see you together with the group soon. We have seven more minutes. And Peter will have one of the final questions. 

Peter Wasilko- https://youtu.be/Puc5vzwp8IQ?t=6148

Peter Wasilko: All right. I was wondering whether you’re doing anything to flag bias of the sources that are behind the sources that are being referenced in the articles. A very common phenomena is that you’ll have some group with the name like, concerned parents trying to improve safety in schools and etc. Then you look at it, and you find out that it’s really a front group for gun manufacturers. Or you’ll have a piece of legislation and the name of the legislation is the exact inverse of what the functional result of the legislation would be. Also, I was wondering whether you’re looking at pop culture as sources of argumentation too. There’s a wonderful wiki called TV Tropes that has links to just about every single movie, book, manga piece of resource out there. Plus they also cross-link examples of those probes in real life. So, you can find whole sections on every TV show that discuss climate change in there. And sometimes, you’ll actually have people use the fictional medium to express policy diagram disputes because they’re afraid that if they put it out on Twitter, they might get cancelled from Twitter, but you can have an alien of some crazy race make the argument in a science fiction story, and you can discuss those social and policy issues that you couldn’t otherwise. The Orville is a great source of those sorts of stories.

Jamie Joyce: First of all, I just want to say, Peter, oh, my god. Thank you so much. I’m so excited for TV Tropes. Thank you. We deconstruct film, but not television shows and non-documentary films. So this actually just may be a whole other source of media that we may really dive into, because I mean, we do archive memes, and we make memes available. So when you said pop culture, I was like, “Oh, yeah. We do the graphic image memes, for sure.” But I didn’t think about, yeah, an alien on a sci-fi film making a critique about something. Didn’t think of that. Thank you very much, Peter. And then as for your other question. Yes. But we have to be careful with labelling. Because labelling is very much a matter of fact and we don’t want to make a mistake and be incorrect about matters of fact. So when we label something opinion, or needs to be fact-checked, or no evidence provided, or this is cherry-picking or something like that, it’s because it is very easily associated with matters of fact. There’s a definition for a thing that’s commonly accepted. This meets a definition (indistinct). But when it comes to astroturfing, predatory journals and things like that, it’s more of a matter of argumentation. And so, we actually do model that in argumentation. So, for example, there’s this whole set of content from this one person who has published only in predatory journals, and we had to deconstruct the website and essentially build out all the argumentation about how the data is not verifiable, the journal in which it was published, it is not credible in these ways. But we had to model that as argumentation. And what we have to do, in terms of our responsibility, is just, we have to make sure when it’s visualized, they pop up at the same time. So it’s not buried, all the counter argumentation that suggests this is invalid it’s, “Hey, because of the severity and weight of the argumentations, they get this other data, you need to see these things at the same time.” So, yeah. We do that. We do our due diligence and look on those lists of suggested predatory journals. And then we check out the website. Did the website, essentially, say it’s pay to publish? Do they have no peer review? Do they have no editorial board? Essentially pick it apart. And then, make sure that, that metadata express through argumentation. Essentially saying this is invalid or, you know…

Peter Wasilko: And it also can be subtle, for this person might have a gorgeous new office in the Pfizer wing of his school and be publishing strong arguments in favour of Pfizer’s latest designer drug. And then, you’d wonder if he did a study that was designed to be able to find problems with that drug, would his school still have the funding to build that building? Or would he suddenly find that the primary source of his salary has gone away? And that might be influencing the way he structures his science. There’s also a community, I think it’s called Tea, that’s looking at reproducibility in science, which you might want to have a look at, if you aren’t already familiar with that work.

Jamie Joyce: There’s a whole bunch of people working on the reproducibility issue. But what are they called? 

Peter Wasilko: Tea, I think is their acronym.

Frode Hegland: Since we started a bit late, we will do a few more minutes. And, actually, Fabien, you go first.

Fabien Benetou- https://youtu.be/Puc5vzwp8IQ?t=6426

Fabien Benetou: Just to bounce back, I also posted on the chat a book on agnotology and the study of political ignorance. I really warmly recommend it. I say warmly even though it’s a horrible thing. But I think it is important. It’s very exciting to hear the process you’re going through. And that prompted me to wonder. So, what I did at some point was, I gathered most of the links of the articles whatever random pages I read. And I put them on my wiki. And what I did was the opposite. Meaning that I have a plugin from the browser that says, “Oh, you’ve already read that page, and that page is on that topic.” So I can browse back to my own notes based on what I’m seeing on my browser, on my screen right now. And I’m wondering if that could, also, be a way, because it sounds like your process is very thorough and could be practical behind, quote-unquote, just the map itself. But on your normal browsing session, being able to connect back to the map, for example. So I’m wondering if you’ve done that? Or if you believe that could be useful to browse the web? And then, as you go through a document, a piece of information that you already analysed, and it’s already referenced, if we could link back and browse the map at a certain point?  I’ll do a bit of promotion for the book I put in the chat, in the meantime. If you’re wondering about the tobacco industry, alcohol industry, and oil industry too. Different sales of Nobel prizes on topics that we’re not necessarily familiar with. And that you see the same heads, that’s a great book. That’s an extremely sad one, but you see the history of convincing people that don’t actually have the expertise but still have the intellectual prestige. So, yeah. Very valuable.

Peter Wasilko: Ah, I found the reproducibility link. I dropped it in the chat.

Jamie Joyce: Yay, thank you. Okay. So, yeah. Short answer, yes, we’ve been thinking about that, Fabien. One of the new tools that we want to build, actually, is going to be a web annotation system. So that people can go all the way through the chain of us collecting content, extracting it. You saw the spreadsheets where we copy things over to text and then deconstruct from there, that’s really lovely because if linked to those things people can see the exact line. But it would be lovely if it was just native. They can go to the archive.org website and see we’ve highlighted this and here is. And then, having that as a plugin that other people can go and traverse and just opt in to see, “Has the Society Library pulled this out?” And seen this somewhere, and implemented this somewhere, is definitely in the timeline of things that we want to incorporate, for sure. Not possibly yet, but yes, we’ve been thinking about it. And here’s the other thing too. I don’t have a huge amount of hope for integrating with Twitter and Facebook and things like that, because I know a lot of friends of mine, who created great products have not been able to get through the door. But that could be a thing too, also, let’s say, if there was a way of detecting the semantic similarity of a tweet with a Society Library snip in it. They do this with Wikipedia on YouTube for example. There’s a particular topic with the, “Here’s the Wikipedia page on it,” in like a bar underneath, to try and promote people going to a source that YouTube finds to be substantial, in order to look and research into that more. And then, it could also be such that, journalists could reference our databases, and essentially, cite it in their news articles and people could link out to go see the Library. So it’s in the creation of new content, it connects to the Library, and then, the Library can also backtrack to, this is where we derive the claims that populate this library in other media content as well. And that’s just web annotation of plugins and things like that.

Frode Hegland- https://youtu.be/Puc5vzwp8IQ?t=6690

Frode Hegland: So final for me, anyway, is the work that I’m doing with my basic software, Author and Reader it’s bizarre. It took me two hours to see how it kind of connected with this. I think it’s probably because what I’m doing is so insanely much simpler than this. But the reason I want to highlight it to ask you a question is, in Author, which is very much targeted at students, part of the writing is for them to define things. If it’s important for them, write what it is. So you would write something like, Doug Engelbart and then a definition. In my case I would write, Doug Engelbart was my friend blah, blah, blah. So it’s personal. It’s not pretending to be objective truth. And then, maybe I’ll mention SRI. If I then somewhere else write SRI, when I then go back to the map, and I move anything I want, click on Doug there will be a line to SRI, purely because the text mentions it. Nothing fancier. But the reason I’m highlighting it to you is that you have this incredibly rich environment, it’s simply that, it seems that, if you make people define things, it helps their own thinking. And if they can then see how they connect. So I’m wondering if you can have a layer within the work you have, or maybe, I’m throwing a 10 million dollar research project at your hair, so I’m not realistically saying, do it now. But you go through this knowledge environment. You pick things up. And you say, “Well, I think this is bullshit, or I think this is important, or I think this relates to…” Whatever it might be. But in a separate space, so that after a while, when they keep doing this, they get a better insight into their heads. And I can see Mark and Antoine making all kinds of head movements because I know it’s related to their work. But I’m wondering if you both have a brief comment and then we need to wind up. 

Jamie Joyce: I’ll just say one thing is that, what you express reminds me of some exercises we do at the Society Library when we teach students about having them extricate what is meant and claims, it’s not the same thing in terms of defining things. But it is an exercise. And I will just say that the feedback we get from students, from performing logical deconstruction exercises pulling out all the claims and media, is that we’ve heard many times that, by the end of the semester they gain a new sight. Because they just inherently see the density of language in a way that they didn’t see it before. And it’s really lovely because so many of them get so excited, and they come back and volunteer for us. They’re really enthusiastic about what they’ve learned, and they find it to be very valuable. So I think that an exercise like that could also be very valuable just based on the feedback we get from our exercises, which are not the same, but similar. And then, what you expressed reminded me of is that, I just had the thought just now, of a new kind of exercise where students could be compelled to be presented an argument, and then counter-argue with it, and then they can also see like how the Society Librarians actually stilllman that argument. So they could work through the database only seeing partial pieces. And are doing their own research and counterarguing. And then comparing that against the professional version. And maybe it’s better, maybe it’s not better. And that could help refine their thinking, as well. So, yeah. I think that those types of exercises really do help people with critical thinking. And I think just increasing their epistemic literacy, as well. Just really knowing how many assumptions that we pack in, to our everyday expressions, and understanding. And we’re forced to extricate that by defining or deconstructing. We really start to appreciate the density and complexity of meaning in knowledge. 

Frode Hegland: Fantastic. I mean, Tools for Thought is part of this big thing. Marc-Antoine, was that your hand or was that a little mouse?

Marc-Antoine Parent: That was me. Sorry, I’m going to diverge a bit. And this is my thinking, not the Society Library. But the definitions, as you put it, is fundamental and what you’re doing, Frode, is helping encourage people to do their local contextual definition. And Jamie’s tool and work certainly does contribute to identifying specific definitions and work. And what I’m currently most interested in is these, how to assemble social definitions from individual definitions? And how to identify how they relate? Where are the differences? Including emerging concept conversations. In some conversations, the concepts are emerging, the definitions are being negotiated, and evolving, and renegotiated. And this is where the ability to show the relationships between the concepts is extremely important. And when I say show the relationship, and that’s another thing I wanted to say to you, I think that the ability to qualify links is prime module. The (indistinct) Latour, who’s a historian of science, has said that erasing the nature of links is one of the great crimes of 21st-century thinking. Saying these things are related. How? Doesn’t matter. That is absolutely terrible. Understanding how things are related is really key to a certain precision of thinking. And having a good epistemology and ontology of how things are related, I think is absolutely fundamental. And how things change when you push them from one domain to another, that’s Latour’s work. When you shift from one definition to another is when there are these shifts of meaning, which are necessary, they’re not always bad. Some of it is confusion, some of it is fluidity. But you need to be able to identify it. and that means naming the relationship.

Jamie Joyce: Yep, I just want to quickly say because this may be relevant to everyone else, the conversation of definitions has come up in communities that Marc-Antoine and I have both been in. And you may all find this to be very interesting that the Society Library approach definitions are also descriptive. So, for example, when we actually create definitions,  we can disambiguate them for the situation. Which is really important in the data architecture. But also, we only give definitions to things that aren’t heavily contested. So the definition of climate change, for example, in the Society Library database, there are 19 different definitions. That’s because it’s such a common phrase, that’s actually just a zip file of 19 different files. It’s the same name for 19 different zip files of meaning of what people apply when they’re using the term. So when it comes to modelling argumentation you can’t just say, “Okay, climate change.” Because so many people are just going to come to that and interpret different meanings. So, we’ve had to find different climate crisis, this is a little bit different than catastrophic climate change, this is a little bit different than global warming. All these different things. And then we’re just gonna have to get to the point where we start creating new names. Like the climate change hoax. All these different small differentiations to let people know that we’re not talking about climate change in the same way at all. So for us that is actually a debate. Our primary question within the climate change database is: what has changed? And there’s 10 different arguments about what climate change actually means and entails and the evidence that support that it’s derived from different media sets, etc. But many of the definitions that are not contested like, what is the Diablo Canyon Nuclear Power Plant? Oh, it’s like a double loop Westinghouse, blah, blah, blah. That’s not really something people argue about, so we just give it its definition.

Frode Hegland: And on that bombshell, thank you very much, Jamie, and everyone else. And we’re here every Monday and Friday. I look forward to having this transcribed, will take a while to organize and clean it up. And continue the dialogue and make amazing things happen. In this community, we’re looking forward to doing some sort of, a flatland, which is what we call, what we’re on now into VR environments. And back again a demonstration of the Future of Text at the end of the year. So it’s very interesting to have the insight, knowledge, and questions from today, and hopefully, some degree of dialogue collaboration. Have a good weekend everyone. 

Jamie Joyce: Thank you all so much for your time. Thanks for having me.

Frode Hegland: Thank you. 

Peter Wasilko: Thank you so much for the talk, I really liked it. 

Marc-Antoine Parent: Thank you, Jamie, it’s great.

Fabien Benetou: Thank you so much. Take care. 

Karl Hebenstreit Jr: Thank you, Jamie.

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