There’s a lot of debate in academic circles about what to call ChatGPT, the new Bing, Bard, and the set of new technologies that operate on similar principles.
“AI” or “Generative AI” are the terms preferred by industry. These terms are rightly criticized by many scholars in the fields of communications, science and technology studies, and even computer science as not only inaccurate (there is nothing “intelligent” about these systems in the way we usually think about intelligence; they have no understanding of the text they produce) but also as crass marketing ploys. These critics say these terms perpetuate a cynical “AI hype cycle” that’s simply intended to drive attention and investment capital to the companies that make these technologies—and deflect attention from their harms.
But I worry that the use of the term “AI hype cycle” isn’t doing the work its proponents think it is. My understanding is that those proponents hope the term will help people see through the breathless predictions of corporate interests and focus instead on the harms these interests are perpetrating in the present. I worry, however, that it reads, to the casual observer, as “nothing to see here.”
Surely that’s not the outcome we want.
“Chatbot” is another contender for the term of choice. And indeed it describes the kinds of interactions that most people are having with these technologies at the moment. But as Ted Underwood has said, “chatbot” doesn’t capture the range of appliacations that these technologies enable beyond chat through their APIs. They’re clearly more than the chatbots we used 20 years ago on AIM.
“LLM” (“Large Language Model”) seems to be preferred by academics (according to Simon Willison’s Mastodon poll), because it’s more accurate. LLM surely does a better job of describing of how these technologies work—by statistically predicting the next most likely word in a sequence—not by suggesting there’s any kind of deeper understanding at work.
But “LLM” has a serious problem. It’s an acronym, and therefore, it’s jargon. And therefore, it’s boring.
That leads me to my title’s second question: “What do we want our words to do?” Is the purpose of our words to asymptotically approach truth above and against all other considerations? Or do we want to draw people’s attention to that truth, even if the words we use are not quite as precise?
I have very mixed feelings about this. LLM is more accurate. But it’s also easy (for ordinary people, decision makers, regulators, politicians) to ignore. That is, it’s boring. I understand not wanting to amplify Silicon Valley’s hype, but we also don’t want to downplay the likely consequences of this technology. There has to be a way to tell people that something will be transformative (quite probably for the worse), and command their necessary attention, without being a cheerleader for it.
For example, the term “World Wide Web” was both completely hype and totally inaccurate when it was coined by Tim Berners Lee in 1989. But after the Internet being ignored by pretty much the entire world for 20 years, “the Web” surely captured the popular mind and brought the Internet to public attention.
Conversely, the terms “SARS-CoV-2” and “COVID-19” were both sober and accurate. But I worry that the clinical nature of the terms enabled people who were already predisposed to looking the other way to do so more easily. Calling it “Pangolin Flu” or “Raccoon Dog Virus” would have been less accurate, but they would have caught people’s attention. “Bird Flu,” “Swine Flu,” and Zika Virus, which have killed very few people in this country get a ton of attention relative to their impact. Surely terminology wasn’t the root cause of our society’s lazy response to the pandemic. But I don’t think it helped.
Now, I am NOT suggesting we develop intentionally misleading terminology just to get people’s attention. We can leave that to Silicon Valley. But I am suggesting that we think about what we want our words to do. Do we value accuracy to the exclusion of all other considerations? In that case, an acronym of some sort may be in order. Or do we also want people to pay attention to what we’re saying?
I don’t have a good suggestion for a specific term that accomplishes both goals (accuracy and attention), but I think we need one. And if we can’t come up with one that does both jobs, and the public conversation settles on “generative AI” or some other term coined by the industry, I don’t think we should spend too much time banging our heads against it or trying to push alternatives. We’ll be better served if our many and valid and urgent criticisms of “generative AI” and its industry are heard by people than by sticking earnestly with a term that lets people ignore us.
If there’s anything we should have learned from the past 25 years of the history of the internet it’s that academics “calling bullshit” is not a plan for dealing with unwanted technology outcomes.
Academics have two very deeply held and interrelated attachments. One is to accuracy and the truth. The other is to jargon. The one is good. The other can cause trouble. I hope in this case our attachment to the first doesn’t lead us to adopt a jargon-y language that enables people already predisposed to ignore the harms of the tech industry to do so more comfortably.
The following is a (more or less verbatim) transcript of a keynote address I gave earlier today to the Dartmouth College Teaching with Primary Sources Symposium. My thanks to Morgan Swan and Laura Barrett of the Dartmouth College Library for hosting me and giving me the opportunity to gather some initial thoughts about this thoroughly disorienting new development in the history of information.
Thank you, Morgan, and thank you all for being here this morning. I was going to talk about our Sourcery project today, which is an application to streamline remote access to archival materials for both researchers and archivists, but at the last minute I’ve decided to bow to the inevitable and talk about ChatGPT instead.
I can almost feel the inner groan emanating from those of you who are exhausted and perhaps dismayed by the 24/7 coverage of “Generative AI.” I’m talking about things like ChatGPT, DALL-E, MidJourney, Jasper, Stable Diffusion, and Google’s just released, Bard. Indeed, the coverage has been wall to wall, and the hype has at times been breathless, and it’s reasonable to be skeptical of “the next big thing” from Silicon Valley. After all we’ve just seen the Silicon Valley hype machine very nearly bring down the banking system. In just past year, we’ve seen the spectacular fall of the last “next big thing,” so-called “crypto,” which promised to revolutionize everything from finance to art. And we’ve just lived through a decade in which the social media giants have created a veritable dystopia of teen suicide, election interference, and resurgent white nationalism.
So, when the tech industry tells you that this whatever is “going to change everything,” it makes sense to be wary. I’m wary myself. But with a healthy dose of skepticism, and more than a little cynicism, I’m here to tell you today as a 25-year veteran of the digital humanities and a historian of science and technology, as someone who teaches the history of digital culture, that Generative AI is the biggest change in the information landscape since at least 1994 and the launch of the Netscape web browser which brought the Internet to billions. It’s surely bigger than the rise of search with Google in the early 2000s or the rise of social media in the early 2010s. And it’s moving at a speed that makes it extremely difficult to say where it’s headed. But let’s just say that if we all had an inkling that the robots were coming 100 or 50 or 25 years into the future, it’s now clear to me that they’ll be here in a matter of just a few years—if not a few months.
It’s hard to overstate just how fast this is happening. Let me give you an example. Here is the text of a talk entitled (coincidentally!) “Teaching with primary sources in the next digital age.” This text was generated by ChatGPT—or GPT-3.5—the version which was made available to the public last fall, and which really kicked off this wall-to-wall media frenzy over Generative AI.
You can see that it does a plausible job of producing a three-to-five paragraph essay on the topic of my talk today that would not be an embarrassment if it was written by your ninth-grade son or daughter. It covers a range of relevant topics, provides a cogent, if simplistic, explanation of those topics, and it does so in correct and readable English prose.
Now here’s the same talk generated by GPT-4 which came out just last week. It’s significantly more convincing than the text produced by version 3.5. It demonstrates a much greater fluency with the language of libraries and archives. It correctly identifies many if not most of the most salient issues facing teaching in archives today and provides much greater detail and nuance. It’s even a little trendy, using some of the edu-speak and library lingo that you’d hear at a conference of educators or librarians in 2023.
Now here’s the outline for a slide deck of this talk that I asked GPT-4 to compose, complete with suggestions for relevant images. Below that is the text of speaker notes for just one of the bullets in this talk that I asked the bot to write.
Now—if I had generated speaker notes for each of the bullets in this outline and asked GPT’s stablemate and image generator, DALL-E, to create accompanying images—all of which would have taken the systems about 5 minutes—and then delivered this talk more or less verbatim to this highly educated, highly accomplished, Ivy League audience, I’m guessing the reaction would have been: “OK, seems a little basic for this kind of thing” and “wow, that was talk was a big piece of milktoast.” It would have been completely uninspiring, and there would have been plenty to criticize—but neither would I have seemed completely out of place at this podium. After all, how many crappy, uninspiring, worn out PowerPoints have you sat through in your career? But the important point to stress here is that in less than six months, the technology has gone from writing at a ninth-grade level to writing at a college level and maybe even more.
Much of the discourse among journalists and in the academic blogs and social media has revolved around picking out the mistakes these technologies make. For example, my good friend at Middlebury, Jason Mittell, along with many others, has pointed out that ChatGPT tends to invent citations: references to articles attributed to authors with titles that look plausible in real journals that do not, in fact, exist. Australian literary scholar, Andrew Dean, has pointed out how ChatGPT spectacularly misunderstands some metaphors in poetry. And it’s true. Generative AIs make lots of extremely weird mistakes, and they wrap those mistakes in extremely convincing-sounding prose, which often makes them hard to catch. And as Matt Kirschenbaum has pointed out: they’re going to flood the Internet with this stuff. Undoubtedly there are issues here.
But don’t mistake the fact that ChatGPT is lousy at some things for the reality that it’ll be good enough for lots, and lots, and lots of things. And based on the current trajectory of improvement, do we really think these problems won’t be fixed?
Let me give another couple of examples. Look at this chart, which shows GPT-3.5’s performance on a range of real-world tests. Now look at this chart, which shows GPT-4’s improvement. If these robots have gone from writing decent five-paragraph high school essays to passing the Bar Exam (in the 90th percentile!!) in six months, do we really think they won’t figure out citations in the next year, or two, or five? Keep in mind that GPT-4 is a general purpose model that’s engineered to do everything pretty well. It wasn’t even engineered to take the Bar Exam. Google CEO, Sundar Pichai tells us that AI computing power is doubling every six months. If today it can kill the Bar Exam, do we really think it won’t be able to produce a plausible article for a mid-tier peer reviewed scholarly journal in a minor sub-discipline of the humanities in a year or two? Are we confident that there will be any way for us to tell that machine-written article from one written by a human?
(And just so our friends in the STEM fields don’t start feeling too smug, GPT can write code too. Not perfectly of course, but it wasn’t trained for that either. It just figured it out. Do we really think it’s that long until an AI can build yet another delivery app for yet another fast-food chain? Indeed, Ubisoft and Roblox are starting to use AI to design games. Our students’ parents are going to have to start getting their heads around the fact that “learning to code” isn’t going to be the bulletproof job-market armor they thought it was. I’m particularly worried for my digital media students who have invested blood, sweat, and tears learning the procedural ins and outs of the Adobe suite.)
There are some big philosophical issues at play here. One is around meaning. The way GPT-4 and other generative AIs produce text is by predicting the next word in a sentence statistically based on a model of drawn from an unimaginably large (and frankly unknowable) corpus of text the size of the whole Internet—a “large language model” or LLM—not by understanding the topic they’re given. In this way the prose they produce is totally devoid of meaning. Drawing on philosopher, Harry Frankfurter’s definition of “bullshit” as “speech intended to persuade without regard for truth”, Princeton computer scientists Arvind Narayanan and Sayash Kapoor suggest that these LLMs are merely “bullshit generators.” But if something meaningless is indistinguishable from something meaningful—if it holds meaning for us, but not the machine—is it really meaningless? If we can’t tell the simulation from the real, does it matter? These are crucial philosophical, even moral, questions. But I’m not a philosopher or an ethicist, and I’m not going to pretend to be able to think through them with any authority.
What I know is: here we are.
As a purely practical matter, then, we need to start preparing our students to live in a world of sometimes bogus, often very useful, generative AI. The first-year students arriving in the fall may very well graduate into a world that has no way of knowing machine-generated from human-generated work. Whatever we think about them, however we feel about them (and I feel a mixture of disorientation, disgust, and exhaustion), these technologies are going to drastically change what those Silicon Valley types might call “the value proposition” of human creativity and knowledge creation. Framing it in these terms is ugly, but that’s the reality our students will face. And there’s an urgency to it that we must face.
So, let’s get down to brass tacks. What does all this mean for what we’re here to talk about today, that is, “Teaching with Primary Sources”?
One way to start to answer this question is to take the value proposition framing seriously and ask ourselves, “What kinds of human textual production will continue to be of value in this new future and what kinds will not?” One thing I think we can say pretty much for sure is that writing based on research that can be done entirely online is in trouble. More precisely, writing about things about which there’s already a lot online is in trouble. Let’s call this “synthetic writing” for short. Writing that synthesizes existing writing is almost certainly going to be done better by robots. This means that what has passed as “journalism” for the past 20 years since Google revolutionized the ad business—those BuzzFeed style “listicles” (“The 20 best places in Dallas for tacos!”) that flood the internet and are designed for nothing more than to sell search ads against—that’s dead.
But it’s not only that. Other kinds of synthetic writing—for example, student essays that compare and contrast two texts or (more relevant to us today) place a primary source in the context drawn from secondary source reading—those are dead too. Omeka exhibits that synthesize narrative threads among a group of primary sources chosen from our digitized collections? Not yet, but soon.
And it’s not just that these kinds of assignments will be obsolete because AI will make it too easy for students to cheat. It’s what’s the point of teaching students to do something that they’ll never be asked to do again outside of school? This has always been a problem with college essays that were only ever destined for a file cabinet in the professor’s desk. But at least we could tell ourselves that we were doing something that simulated the kind of knowledge work they would so as lawyers and teachers and businesspeople out in the real world. But now?
(Incidentally, I also fear that synthetic scholarly writing is in trouble, for instance, a Marxist analysis of Don Quixote. When there’s a lot of text about Marx and a lot of text about Don Quixote out there on the Internet, chances are the AI will do a better—certainly a much faster—job of weaving the two together. Revisionist and theoretical takes on known narratives are in trouble.)
We have to start looking for the things we have to offer that are (at least for now) AI-proof, so to speak. We have to start thinking about the skills that students will need to navigate an AI world. Those are the things that will be of real value to them. So, I’m going to use the rest of my time to start exploring with you (because I certainly don’t have any hard and fast answers) some of the shifts we might want to start to make to accommodate ourselves and our students to this new world.
I’m going to quickly run through eight things.
The most obvious thing we can do it to refocus on the physical. GPT and its competitors are trained on digitized sources. At least for now they can only be as smart as what’s already on the Internet. They can’t know anything about anything that’s not online. That’s going to mean that physical archives (and material culture in general) will take on a much greater prominence as the things that AI doesn’t know about and can’t say anything about. In an age of AI, there will be much greater demand for the undigitized stuff. Being able to work with undigitized materials is going to be a big “value add” for humans in the age of these LLMs. And our students do not know how to access it. Most of us were trained on card catalogs, in sorting through library stacks, of traveling to different archives and sifting through boxes of sources. Having been born into the age of Google, our students are much less good at this, and they’re going to need to get better. Moreover, they’re going to need better ways of getting at these physical sources that don’t always involve tons of travel, with all its risks to climate and contagion. Archivists, meanwhile, will need new tools to deal with the increased demand. We launched our Sourcery app, which is designed to provide better connections between researchers and archivists and to provide improved access to remote undigitized sources before these LLMs hit the papers. But tools like Sourcery are going to be increasingly important in an age when the kind of access that real humans need isn’t the digital kind, but the physical kind.
Moreover, we should start rethinking our digitization programs. The copyright issues around LLMs are (let’s say) complex, but currently Open AI, Google, Microsoft, Meta, and the others are rolling right ahead, sucking up anything they can get their hands on, and processing those materials through their AIs. This includes all of the open access materials we have so earnestly spent 30 years producing for the greater good. Maybe we want to start asking ourselves whether we really want to continue providing completely open, barrier-free access to these materials. We’ve assumed that more open meant more humane. But when it’s a robot taking advantage of that openness? We need a gut check.
AIs will in general just be better at the Internet than us. They’ll find, sort, sift, and synthesize things faster. They’ll conduct multi-step online operations—like booking a trip or editing a podcast—faster than us. This hits a generation that’s extremely invested in being good at the Internet, and, unfortunately, increasingly bad at working in the real world. Our current undergraduates have been deeply marked by the experience of the pandemic. I’m sure many of you have seen a drastic increase in class absences and a drastic decrease in class participation since the pandemic. We know from data that more and more of our students struggle with depression and anxiety. Students have difficulty forming friendships in the real world. There are a growing number of students who choose to take all online classes even though they’re living in the dorms. This attachment to the virtual may not serve them well in a world where the virtual is dominated by robots who are better than us at doing things in the digital world. We need to get our students re-accustomed to human-to-human connections.
At the same time, we need to encourage students to know themselves better. We need to help them cultivate authentic, personal interests. This is a generation that has been trained to write to the test. But AIs will be able to write to the test much better than we can. AIs will be able to ascertain much better than we can what they (whomever they is: the school board, the college board, the boss, the search algorithm) want. But what the AI can’t really do is tell us what we want, what we like, what we’re interested in and how to get it. We need to cultivate our students’ sense of themselves and help them work with the new AIs to get it. Otherwise, the AI will just tell them what they’re interested in, in ways that are much more sophisticated and convincing than the Instagram and TikTok algorithms that are currently shoving content at them. For those of us teaching with primary sources this means exposing them to the different, the out of the ordinary, the inscrutable. It means helping them become good “pickers” – helping them select the primary sources that truly hold meaning for them. As educators of all sorts, it means building up their personalities, celebrating their uniqueness, and supporting their difference.
I think we also need to return to teaching names and dates history. That’s an unfashionable statement. The conventional wisdom of at least the last 30 years is that that names, dates, and places aren’t that important to memorize because the real stuff of history are the themes and theories—and anyway, the Google can always give us the names and dates. Moreover, names and dates history is boring and with the humanities in perpetual crisis and on the chopping block in the neoliberal university, we want to do everything we can to make our disciplines more attractive. But memorized names, and dates, and places are the things that allow historians to make the creative leaps that constitute new ideas. The biggest gap I see between students of all stripes, including graduate students, and the privileged few like me who make it into university teaching positions (besides white male privilege) is a fluency with names, dates, and places. The historians that impress most are the ones who can take two apparently disconnected happenings and draw a meaningful connection between them. Most often the thing that suggests that connection to them is a connected name, date, place, source, event, or institution that they have readily at hand. Those connections are where new historical ideas are born. Not where they end, for sure, but where they are born. AI is going to be very good at synthesizing existing ideas. But it may be less good at making new ones. We need students who can birth new ideas.
Related to this is the way we teach students to read. In the last 20 years, largely in response to the demands of testing, but also in response to the prioritization of “critical thinking” as a career skill, we’ve taught students not to read for immersion, for distraction, for imagination, but for analysis. Kids read tactically. They don’t just read. In many cases, this means they don’t read at all unless they have to. Yet, this is exactly how the AI reads. Tactically. Purely for analysis. Purely to answer the question. And they’ll ultimately be able to do this way better than us. But humans can read in another way. To be inspired. To be moved. We need to get back to this. The imaginative mode of reading will set us apart.
More practically, we need to start working with these models to get better at asking them the right questions. If you’ve spent any time with them, you’ll know that what you put in is very important in determining what you get out. Here’s an example. In this chat, I asked GPT-3.5, “How can I teach with primary sources.” OK. Not bad. But then in another chat I asked, “Give me a step-by-step plan for using primary sources in the classroom to teach students to make use of historical evidence in their writing” and I followed it up with a few more questions: “Can you elaborate?” and “Are there other steps I should take?” and then “Can you suggest an assignment that will assess these skills?” You’ll see that it gets better and better as it goes along. I’m no expert at this. But I’m planning on becoming one because I want to be able to show our students how to use it well. Because, don’t fool yourselves, they’re going to use it.
Finally, then, perhaps the most immediate thing we can do is to inculcate good practice around students’ use of AI generated content. We need to establish citation practices, and indeed the MLA has just suggested some guidance for citing generative AI content. Stanford, and other universities, are beginning to issue policies and teaching guidance. So far, these policies are pretty weak. Stanford’s policy basically boils down to, “Students: Don’t cheat. Faculty: Figure it out for yourselves.” It’s a busy time of year and all, but we need urgently to work with administration to make these things better.
I’m nearly out of time, and I really, really want to leave time for conversation, so I’ll leave it there. These are just a couple of thoughts that I’ve pulled together in my few weeks of following these developments. As I’ve said, I’m no expert in computer science, or philosophy, or business, but I think I can fairly call myself an expert in digital humanities and the history of science and technology, and I’m convinced this new world is right around the corner. I don’t have to like it. You don’t have to like it. If we want to stop it, or slow it down, we should advocate for that. But we need to understand it. We need to prepare our students for it.
At the same time, if you look at my list of things we should be doing to prepare for the AI revolution, they are, in fact, things we should have been (and in many cases have been) doing all along. Paying more attention to the undigitized materials in our collections? I’m guessing that’s something you already want to do. Helping students have meaningful, in-person, human connections? Ditto. Paying more attention to what we put online to be indexed, manipulated, sold against search advertising? Ditto. Encouraging students to have greater fluency with names, dates, and places? Helping them format more sophisticated search queries? Promoting better citation practice for born-digital materials and greater academic integrity? Ditto. Ditto. Ditto.
AI is going to change the way we do things. Make no mistake. But like all other technological revolutions, the changes it demands will just require us to be better teachers, better archivists, better humans.
Check out these amazing WPA-style posters created by the Department of Energy to mark the infrastructure achievements made possible under the 2009 stimulus bill. I hope this time around, the government doesn’t wait 10 years to start selling the infrastructure and climate bills that passed earlier this year.
Two takes on this year’s tech industry crash: The first, from Derek Thompson, is cultural (the crash is big tech’s “midlife crisis”). The second, from Matt Yglesias, is financial (higher interest rates are making speculation in technology relatively less attractive).
Steven Johnson on the importance of the cassette tape and the way it changed both the sound and the business of music—in many of the same ways that another low-fidelity technology, the mp3, did.
Finally, if you have been wondering what Post.news is, how it’s different from other social networks, and especially how it plans to make money, here’s a primer from Neiman Journalism Lab.
It looks like the theme of this week’s Briefly Noted post is Substack. I didn’t intend it, but each of the following is taken from the platform:
Substack is launching a new “letters” feature to support epistolary blogging. Like most things Substack, I love the idea, but I hate the paywall, and I worry about long term preservation and access. Epistolary scholarship has a long tradition in the humanities (St. Paul is a pretty decent example), and like blogging, I’m glad to see it making a comeback, just not on a proprietary platform.
Did you know Gettysburg still invites confederate reinactors to march in its Remembrance Day parade, battle flags and all? Neither did I. Kevin Levin at Civil War Memory writes: “Every year Confederate reenactors are invited to march alongside United States soldiers in Gettyburg’s Remembrance Day Parade, which commemorates Lincoln’s famous address. That’s right. On the same day that the community gathers to reflect on Lincoln’s words, Confederate flags are marched through the streets.”
Kareem Abdul-Jabar is the best. Here he is on forgiveness: “I see people constantly saying, ‘I forgive but I don’t forget,’ which they think makes them both moral and tough. Actually, they are neither. The phrase means the exact opposite of forgiving. To forgive is to forget the transgression in order to start fresh.”
Ryan Cordell posted his remarks from the 30 Years of Digital Humanities at UVA conference. He makes some great points about the importance of collaboration in digital humanities. One thing he says, that I’ve often thought myself, is how bad the standard DH curriculum is at teaching collaboration to students. It’s very hard to teach the ins-and-outs, ups-and-downs of working in a team in a one semester classroom course, especially when each student must be graded individually. We can teach skills in the classroom, but we all know that’s not really what matters for successful digital humanities work. That’s why centers, notwithstanding recent critiques of them, are so important and why we have to do a better job of integrating our classroom teaching and center-based research.
I don’t think Ezra Klein reads this blog, but if he does, I want to tell him that he should do more Q&A episodes with Aaron Retica, his editor, as he did in his post-election podcast. It’s great to hear Ezra’s opinions unfiltered by his interactions with a guest, and Aaron Retica’s questions (and voice!) are challenging and insightful. It’s obvious they make a great team.
Speaking of Klein, like him and his old partner, Matt Yglesias, I’m extremely skeptical of the hype around Ron DeSantis’s presidential prospects. It’s true that he scored a big win in a purplish state. He’s certainly a talented politician. But his win wasn’t bigger than other purplish state Republican governors (Dewine in Ohio or Sununu in New Hampshire). Furthermore (and I don’t hear anyone talking about this) DeSantis was running against, for all intents and purposes, another Republican—former Republican-governor-turned-Democrat Charlie Crist. The conventional wisdom was that running Christ against DeSantis would peel off moderate Republican votes. Did no one remember that the same strategy lost him and the Democrats the governor’s race to Rick Scott in 2014? Did no one imagine that running a former Republican might also depress Democratic votes? What committed Democrat wants to vote for a guy they hated a few short years ago just because he changed the letter in front of his name? It would be like asking Democrats to vote for Paul Ryan for President in 2024 because he didn’t go full-MAGA and might pick up some disgruntled Republican and Independent swing votes. I’m all for running moderate Democratic candidates in the Biden mold. But if they’re going to be right of the party’s base on policy, then they absolutely must be trusted party members. People want to know what side they’re voting for. In any event, I don’t think DeSantis’s victory is all it’s being cracked up to be. He ran a good race against a bad candidate with the underlying national fundamentals (inflation, crime, etc.) as wind at his back. That puts him in the conversation for 2024, but it doesn’t make him a strong favorite for the nomination, much less the presidency. Remember President Scott Walker? Neither do I.
I’m brand new to Mastodon. Many of us are. This might suggest that we shouldn’t have opinions. But I think the opposite is true. If Mastodon is truly a decentralized platform, if it’s truly designed to support distinctive communities and their distinctive needs, then we, as a community of humanists, should decide how we’re going to use it. We should start doing it now, before it gets away from us.
Deciding how we want to use it—what Mastodon will mean to us—means not putting too much stock in the “norms” and “rules” that other communities have established on the site. That is not to say we should be bulls in the porcelain shop (or as Shawna Ross tooted, we “don’t want to go all Kool-Aid man”), or that we should be disrespectful to other, more established communities and their needs and concerns. As always, we should approach our work, our tools, and our public engagements with humility. But it’s legitimate for us to use the technology to meet our needs and concerns, needs and concerns that have for too long gone unmet by Twitter, needs and concerns that may not be the same as other, older Mastodon communities.
In that spirit, here are a few early thoughts on how I think we should use Mastodon to build a supportive, inclusive, interesting, and useful thing for the humanities community.
First, you should join a server (e.g. hcommons.social) where a lot of other humanists can be found, and spend most of your time in your “local” or “community” timeline/tab. It is all well and good to follow people from other servers, and you should keep up with friends and happenings in those other places. But if you’re on the right server, your main source of serendipity, delight, information, and community will come from that local timeline. If your server’s local timeline is not delivering those things, find another server.
Second, and relatedly, you should mostly avoid the “the fediverse” (i.e. the feed of posts aggregated from across Mastodon’s servers found in the “federated” or “all” tab in your app). It seems to me that in time this aggregated feed will just reproduce Twitter, in all its disorienting chaos and vitriol. It probably won’t be quite so bad because it won’t have an algorithm pushing ads and outrage down your throat. But there’s bound to be plenty of ugly distraction nonetheless.
Third, and this is bound to be controversial, but don’t be too fussed about content warnings (CW’s), except insofar as you think members of your local server will appreciate them. That is, I wouldn’t be too worried about sticking to the “norms” or “best practices” that other, earlier communities on Mastodon have established. I appreciate that these norms are in place because Mastodon has been a refuge for marginalized BIPOC, LGBTQ+, and other communities—and I think we want to be a refuge for members of those communities too. But we shouldn’t simply adopt the practices of the early adopters because they say we should. We should decide the ways in which we want to use the tools Mastodon gives us to support our aims, including, but not limited to, our aims of diversity, equity, and inclusion. So, for example, I think it’s totally fine to use the CW feature to truncate and expand a long toot. One of the distinctive features of the humanities community is its tolerance for difference. Another is its longwindedness. It’s OK to use the tool to support both things!
Fourth, let’s start blogging again. One of the great things about early #DH Twitter was that we were all still blogging. Twitter became a place where we could let a wider audience know that we blogged something and then support a discussion around that something that was more freeflowing than the blog’s own comments thread could support. Let’s bring that practice back! One easy step would be to stop posting long, narrative threads (i.e. tweets “1/27”) to social media. Instead just post a title, a one sentence description, a link to your post with a #blogpost hashtag, and an invitation to discuss. If we could use Mastodon to reinvigorate the culture of humanities blogging, that would be an amazing success.
Fifth, keep politics to a minimum. It’s not that we should never talk about politics, but reworking takes that one can get elsewhere in the media (cable news, the op-ed pages, Twitter, etc.) isn’t going to make this a nicer place to be. If you’re going to get political, clearly tie it to your research, teaching, public humanities practice, or something else that connects you to the community that your local server is intended for. Otherwise, set up another account on another, more clearly political server, and post there.
Those are just some early thoughts. I’ll probably follow up in the next week or so with some more. In the meantime, I’d love to hear yours.
Taylor Swift told us in the Folklore studio movie that the 5th track on each of her albums holds a special meaning for her. It wasn’t exactly a secret, but the film confirmed it. The tracks include some of her best: “All Too Well”, “Dear John”, “Tolerate It.” Here’s a Spotify playlist of Swift’s 5th songs. The latest, “You’re on Your Own, Kid,” is the highlight of her new album.
If you like Marketplace on NPR, listen to this recent episode of the Pivot podcast. Kai Ryssdal, the host of Marketplace, joins Kara Swisher to discuss the latest business/tech stories. It’s great to hear Ryssdal’s distinctive voice in this more free flowing, opinion laden format.
With news that the new owner of Twitter has decided to sell verified accounts, I’ve decided to keep @foundhistory to prevent anyone from impersonating me (though Lord knows who would want to). You won’t see me tweeting for the reasons I laid out last week, but I figure if I’m keeping the account, I might as well push links to posts on this blog to the platform for those of you who remain there.
More Twitter: Zeynep Tufekci on ad-supported social networking sites (Twitter in particular): “Humans have strong in-group and out-group tendencies — sociology-speak for my team versus your team…. If you want to keep a group of people engaged, fueling that group competition is a pretty good method…. This means that whatever the topic, by design and by algorithm, social media often elevates the worst, most divisive content…. This is an infrastructure of authoritarianism, created to deliver ads more effectively. It’s a terrible model for the digital public sphere.” I spent an hour in my class today trying to explain this dynamic, and Zeynep just did it in a paragraph. Sigh.
And in non-Twitter news: UConn is launching a new streaming service for its athletics program, the first in the nation.
Nearly 200 years ago, the United States promised to seat a delegate from the Cherokee Nation in Congress as part of the treaty that forcibly removed the tribe from Georgia to Oklahoma. There’s a renewed movement to make good on that promise. The United States should meet its treaty obligations. It’s not just a matter of justice for indigenous peoples (though that’s the most important part). It’s about being a nation that deserves the trust of its citizens and the world. In that way it’s part and parcel of the fight against creeping authoritarianism in this country. Autocrats lie, cheat, and steal. Liberal democracies aren’t supposed to do that.
Forbes reports this morning that a Beijing-based team at TikTok had plans to monitor the physical location of specific Americans. No word yet on which Americans or whether they are members of the government. I’ve been banging the “TikTok is dangerous” (and not just for teen self-esteem) drum for years now to eye rolls from friends and colleagues. This isn’t quite an “I told you so” moment, but it’s getting close.