Fall 2024 Courses


Here are the courses I am teaching this fall. The 432 is a regular feature now in our folklore course offerings and I have taught it with a focus on legends and information cascades for a few years now. The 370 is a new course, and I have to say I am looking forward to teaching a course with a non-folklore focus. I don’t get to do this often, and I am excited to see what students bring to the table.

ENGL 370. Interactive Fiction & Narrative Games. Branching narratives, interactive fiction, text adventures, CYOA all describe a form of entertainment—be it literary, performed in a group, or in a video game—in which a reader is given choices and their choices determine the nature and outcome of the story. This course explores the history of narrative games, from collaborative storytelling in oral cultures to the robust open-world games to cinematic universes in which multiple storylines exist (and sometimes interact). Course inputs include reading, viewing, and playing. Course outputs include analytical explorations of forms and mechanisms and the development of fictions of your own.

ENGL 432. American Folklore. The subtitle of this course is “Legends, Conspiracy Theories, Cryptids, Oh My!” This course seeks to explore the world in which all of us are already immersed, an online sea of information and misinformation. What are the impulses behind these flows, and what are their diverse functions. From the moment that humans became capable of re-presenting reality, we were engaged in various forms of fiction. Some forms are obviously meant for entertainment, like tales and jokes, and other forms are meant to inform and guide us, like myths and histories. In-between are the stories we tell, the information we pass along, and the arguments we make in which we conjecture about the nature of reality. Individuals interested in this course should be aware that there is as much darkness as light in what we consider and should be prepared to handle topics objectively.

A Star to Steer By


This semester I am teaching a class on Project Management in Humanities Scholarship. I have seen enough graduate students stumble when shifting from the managed research environment of course papers to the unmanaged research environment of the thesis or dissertation, that I thought it would be useful to try out some of the things we know about how best to manage projects in general as well as offer what I have learned along the way. The admixture of experts agree this works and this works for me I hope opens up a space in which participants can find themselves with a menu of options from which they feel free to choose and try. Keep doing what works. Stop doing what doesn’t.

We are a month into our journey together and almost everyone has finally acceded to the course’s manta of doing something is better than doing nothing (because the feeling of having gotten anything done can be harnessed to build momentum to get something more important done), but there are a few participants who are still frozen at the entry door to the workshop where each of us, artisan-like, is banging on something or other.

All of them have interesting ideas, but some are struggling with focus. I think this is where the social sciences enjoy an advantage. They have an entire discourse, which is thus woven into their courses and their everyday work lives, focused on having a research question. What that conventionally means is that you start with a theory (or model) of how something works; you develop a hypothesis about how that theory applies to your data (or some data you have yet to collect because science); and then you get your results in which your hypothesis was accurate to a greater or lesser degree.

Two things here: the sciences have the null hypothesis, which means they are (at least theoretically) open to failure.1 The sciences also have degrees of accuracy. Wouldn’t it be nice if we could say things like “this largely explains that” or “this offers a limited explanation of that” in the humanities? Humanities scholars would feel less stuck because they would be less anxious about “getting it right.” We all deserve the right to be wrong, to fail, and we also deserve the right to be sorta right and/or mostly wrong. Science and scholarship are meant to be collaborative frameworks in which each of us nudges understanding just that wee bit further. (We’re all comfortable with the idea that human understanding of, well anything, will never be complete, right? The fun part is the not knowing part.)

The null hypothesis works very clearly when you are working within a deductive framework but it is less clear when you are working in an inductive fashion. Inductive research usually involves you starting with some data that you find interesting, perhaps in ways that you can’t articulate and your “research question” really amounts to “why do I find this interesting?” Which you then have to translate/transform into “why should someone else find this interesting?” Henry Glassie once explained this as the difference between having a theory and needing to data to prove it, refine it, extend it and having data and needing to explain it.

There is also a middle ground which might be called the iterative method, wherein you cycle between a theory or model, collecting data, and analyzing that data. Each moment in the cycle helps to refine the others: spending time with the data gives you insight into its patterns (behaviors, trends) which leads you to look into research that explores those patterns, trends, behaviors. Those theories or models then let you see new patterns in your texts that you had not seen before, or, perhaps, make you realize that, given your interest in this pattern, maybe you need different texts (data) to explore that idea.

I see a lot of scholars, junior and senior, stuck in the middle of this iterative method without realizing it and don’t know which moment to engage first. What should they read … first? (I have seen the panic in their faces.) What I tell participants in this workshop is that it doesn’t matter. They can start anywhere, but, and this is important, start. No one cares whether you start reading a novel (and taking notes) or reading an essay in PMLA (and taking notes). 99% of managing a project as an independent researcher is just doing something and not letting yourself feel like you don’t know where to start. Just start.

Will it be the out come be the project they initially imagined? Probably not. But let’s be honest, that perfect project they initially imagined lived entirely in their heads—as it does for all of us. It was untroubled by anything like work. (That’s what makes it ideal!) It was not complicated by having to determine where we might publish the outcome, who might be interested, to what domain you might contribute. It was also unavailable to anyone else, inaccessible to anyone else, and probably incomprehensible to anyone else. As messy and subpar as the things we do in the hours we have are, in comparison to that initial dream, they are at least accessible to others, who will probably find them interesting and/or useful.

To be clear, I usually press workshop participants and students to start with data collection / compilation (and not with a theory). Mostly that’s because I am a folklorist (and some time data scientist) and I feel at my most driven when a real work phenomena demands that I understand it. To a lesser extent, as comfortable as I am with my own theoretical background, I find the current explosion in all kinds of theories a bit overwhelming. I prefer to let the data tell me what data I need to go learn, else I might end up going down the rabbit hole of great explanations and never get anything done!

  1. The sciences are currently undergoing a pretty severe re-consideration of the “right to be wrong.” With the cuts in funding to so many universities — because, hey, the boomers got their almost free ride and shouldn’t have to pay for you — the American academy has shrunk, creating greater competition for the jobs that remain, which has meant that scientists often feel like they can’t fail. Failure must be an option when it comes to science, and scholarship. When it isn’t, we end up with data that has been, perhaps purposefully or perhaps unconsciously, miscontrued because the results need to be X. 

Test File


This is for the text analytics class: here is the file you are looking for.

Raising a World Builder


A recent comment I made on the current state of education in the humanities on LinkedIn drew a fair amount of attention. I’m not linking to that comment here as it was of a moment, but there are some things I have observed based both on being a parent of a particular kind of thinker as well as documenting similar kinds of thinkers out in the world. I call them world builders here, but they might also be called immersive thinkers.


In the car one morning on the way to her school I commented to my daughter that the rain had made driving a bit more difficult than usual and that I would have to make sure to keep two hands on the wheel. It was, for me in that moment, simply a metonym for paying attention, and, I confess, a way of letting my daughter know that her dad may not be paying as close attention to our conversation as we both often enjoyed. Over the years of a morning commute that got her to school and me to work, we had enjoyed a wide variety of conversations, which sometimes ran sufficiently wild, especially at her end, that I had to remind her, as a way of reminding myself, that driving was the higher priority.

A little too often my reminders came out more as a chides, which I always regretted. As was often (thankfully) the case, my daughter performed some conversational judo on it by responding, “What if you had three hands?” Her first thought was that I could drive and wave to drivers nearby, but quickly she spun the idea out into a variety of possibilities before settling down into playing a variety of instruments with three hands: there was a three-handed piano piece, then a three-handed guitar melody, and then a three-handed trumpet call. The sounds grew wilder, weirder and her laughter built from giggles to squeals.

Her first move displayed the power of divergent thinking, something which has been explored quite a bit over the past few decades in creativity studies, but her next move was to dwell in a particular domain, to immerse herself in a world, and to play with the possibilities there. For the time being, I would like to call that immersive thinking. It is surely related to that kind of thinking that we sometimes call rich mode or right brain thinking in a way that I want to spend more time thinking about — and to which I am open to suggestions![*]

World-building was, and is, like a reflex action for my daughter. From the time she could speak, she spun out stories. She usually enacted the stories, dramatizing them with props and costuming if she was a character or animating a wide variety of objects, some of them more obviously meant for such use and others not. I can’t, for example, count the number of times objects at restaurant tables came to life and led complex social lives when adult conversation became uninteresting to her. My wife and I saw utensils be sisters, salt and pepper shakers be parents, and a tented napkin become a home.

It was, and is, an amazing thing to watch, but as many creative individuals know, such an ability does not come without its penalties. While her school labeled her a “deep creative,” it seemed largely a way of admitting they were unable to come up with a plan on how to make a space within which she could learn and grow to suit her own abilities and interests. Don’t get me wrong: she did well (enough) in school, but that’s largely because we worked hard at home for her to adapt to the regimen at school. And so she got high marks, but those marks were also regularly accompanied by comments from, well-meaning and really nice, teachers that she “did not pay attention” as well as she should, that she was “daydreamy” or that “sometimes she just phones it in.”

One could perhaps fault the teachers, but I rarely find individuals are the problem in these circumstances. More often a system is at work. In this case, I think it’s fair to blame a larger educational ideology that has come to rely upon standardized tests as one of its central metrics. In a moment that resembles the classical economics parables about unintended consequences, what we so many of us face, as parents in the paroxysms of our children or ourselves, is an entire educational system which many believe is headed precisely in the wrong direction for what looks like reasonable, well, reasons.

Indeed, an entire cluster of industries have arisen around the wobbling of the educational infrastructure in our country. The technorati favor two flavors that are not necessarily mutually exclusive. The first flavor is that articulated by Ken Robinson who argues that our schools are stuck in the industrial age, anxiously trying to turn out uniform widgets in a moment where standardization couldn’t be less useful – the assumption being that things are changing more quickly and more predictably than ever. I don’t subscribe fully to this latter notion, but it’s not hard to see that the current context for businesses favors only a few large incumbents with stability, but employment with those incumbents, as two decades of layoffs and jobs moving from one part of the world to another have provied, is not stable. In other words, institutions have stability, but only individuals at the top of those institutions get to enjoy the fruits of that stability.

Outside of those narrow mountaintop retreats, there’s a whole host of changes taking place as industries transform in the face of an amazing amount of computing power. My own industry, higher education, is facing such a transition, but think about even the way manufacturing is changing as building components becomes less about removing metal by mill and lathe work or stamping and cutting but more about “printing” them by building up a part molecule by molecule. Suddenly, economies of scale matter less and sheer imagination matters more. (Well, you’ll still need quite a bit of capital to have such a “printer” at your disposal, but that’s a return to a history we have seen already – i.e., the original printing press!)

What to do with our little geek, our world builder?

Here’s the short of it: our daughter was a geek. She had all the classic geek traits: she prefered to be fully immersed in a problem or project or world and she oscillated between wanting external affirmation for her accomplishments and not caring what others think. Most geeks I know are like this. Many of them truly believe they don’t need anyone’s approval, and for a few of them that may very well be true. I also know, speaking as a geek (I think) myself, that, yes, sometimes a nod from someone you respect is not only all you need, but it is something you really want.

A lot of curricula which have high geek probabilities have switched to more project-oriented pedagogies. We are seeing more of it engineering, and it has always been a prominent part of architecture. But what to do with our geeks, our world builders in other domains? How do we re-rig systems at least to allow them to think the way they think?

An example from her experience:

For a time, our daughter was in the school choir. Every year the choir put on a musical. One year it was Charlie and the Chocolate Factory; another it was The Wizard of Oz. Every year students auditioned for a role in the play. Now, how do you suppose those auditions took place? Did it come after a watching the film version or reading all or parts of the book? Did it come after listening to some of the story’s most famous passages and songs? That is, did it allow an immersive thinker an opportunity to do what they do best, get inside a world and look around, elaborate it, play with it? No, the auditions were songs from some place else, handed out the week or so before the auditions. Students were told to practice the songs, do their best, and decisions would get made.

Now, that approach works if a student is procedurally-driven and understands the necessity, or already desires, adult approval. It doesn’t work at all for the student that needs to live and breathe inside a thing, to get a sense of it, to find their excitement there.

Fundamentally, this comes down to the difference between teachers as the center of a curriculum and students at the center. As a teacher myself, I know I can’t be all things to all students, and in a post to follow, I want to think more about how education might be made better for more kinds of learners than it currently is. In fact, I worry about one recent trend in particular: the rise of the master teacher and what that means for learning differences — here, learning differences are meant much more broadly than they are in the education industry.

Telling Stories with Data


In Fall 2023 I led a course on digital storytelling. In preparing for the course, I wanted to see what others were doing, and so I searched for course listings, tracked down syllabi, and compared assignments and foci. It was fascinating to see the range of things being done. One thing that I did not fully expect was how often a search for “digital storytelling” washed me up on data science beaches. The graphic below tells the story, but I also want to collect more links to see what I can learn. (See the list below.)

Storytelling among the Four Pillars of Data Science

Copyright and AI


Some time last year, comments were requested on the matter of AI and copyright. I submitted the following.

I am writing for myself, but as a folklorist I am also writing with profound respect, and sadness, for our national tradition of enabling private profit at the cost of the public commonwealth. Like the pharmaceutical industry raiding traditions around the world in order to develop better, perhaps life-changing, medicines, we have allowed the large language models behind most of the more prominent AI platforms to harvest knowledge of a lot of individuals without the individuals themselves receiving any acknowledgment, compensation, or share in the profit. Whether we call it “folk” or “mass,” we dis-enfranchise those who actually produce the materials from which we derive products.

We cannot fall back on user agreements which, in order for the basics of the web to work, had individuals consent to broad grants of copyright. We must acknowledge that most users posted texts, images, and other media assets to various platforms and sites in the interest of creating and maintaining various communities. That they were willing to be sold to advertizers, because that is the basis for American media production, should not in any way affect our consideration that their materials, and thus the people themselves to some degree, can simply be given to AI platforms. At least the social media platforms gave them something of value in exchange. AI platforms are already monetized, seeking rent for creating an abstraction of a city built of neighborhoods built by others.

We cannot know what will be the eventual outcome of the development of these AI platforms, and I don’t think referencing the hype or the fear-mongering does any good here. What we can know is that a system’s integrity must be clear and checked throughout the process. Right now, we can say for certain that these systems were built without integrity when it comes to their data acquisition. If we do not figure this out, if we do not create useful guidelines for clarity and integrity, than we are somewhat dooming these systems to have further negative impacts.

Comment Tracking Number: lm9-e4zx-p2oy

A Statistical Ouroborus


I’m preparing to teach text analytics, the first time such a course has been offered at my university. I came across this great moment in John Scalzi’s Redshirts where statistical analysis is mentioned, but I can’t find a way to include it in the syllabus:

“So what you’re saying is all this is impossible,” Dahl said.

Jenkins shook his head. “Nothing’s impossible,” he said. “But some things are pretty damned unlikely. This is one of them.”

“How unlikely?” Dahl asked.

“In all my research there’s only one spaceship I’ve found that has even remotely the same sort of statistical patterns for away missions,” Jenkins said. He rummaged through the graphic elements again, and then threw one onto the screen. They all stared at it.

Duvall frowned. “I don’t recognize this ship,” she said. “And I thought I knew every type of ship we had. Is this a Dub U ship?”

“Not exactly,” Jenkins said. “It’s from the United Federation of Planets.” Duvall blinked and focused her attention back at Jenkins. “Who are they?” she asked.

“They don’t exist,” Jenkins said, and pointed back at the ship. “And neither does this. This is the starship Enterprise. It’s fictional. It was on a science fictional drama series. And so are we.”

How to be bored


In a response to a video by Parker Settecase on the utility of boredom and of capitalizing on it by using a notebook, theorangecatmom noted:

I’m not that smart, but my Dad taught me to use my brain to entertain myself when bored pre-cellphones. I still make myself practice it when I’m in a waiting room or sometimes on my breaks at work. As a kid, he taught me to count things, find patterns in the stuff around me, play mental math games, stuff like that. As an adult, when I’m surrounded by people, I sometimes just listen or watch what they’re doing and think about why they might be doing it. I call it practicing being bored and it blows people’s minds that I do it on purpose.

Speaking Subjects, Subjects Spoken: Using TED Talks to Understand Discursive Gender Formations


K.M. Kinnaird, Allison Chaney, and I have submitted our latest work with/on TED talks to the Journal of Cultural Analytics. For those interested and wanting to know more about what we have been up to for all these many months, here’s the introduction:

Mappings of texts to assigned or assumed genders qua gendered have been a part of studies of linguistic expressivity since Robin Lakoff speculated about the differences between men and women’s ways of speaking (Lakoff 1975). Like others, we find such explorations of differences in expression between men and women compelling, whether it is focused on modal meaning — expressions of a speaker’s certainty, or uncertainty found in tentative language like hedges, tag questions, intensifiers — or in affective meaning — expressions of a speaker’s attitude toward his/her audience which can be mapped to group composition, the relationship among participants, and their status.1 Few studies have been as clear-cut in their findings as Robin Lakoff’s initial speculations suggested, but the larger field of inquiry she engaged has enriched a variety of philological domains both in terms of gender but also in terms of power dynamics across and within groups. This inquiry has run either parallel to or resulted in far more nuanced appreciations of the ways that texts occur, the situations in which they occur, and how the texts themselves are either shaped by an event or shape the event in some fashion. As Patricia Sawin notes in her consideration of gender and power in situations where texts feature: “esthetic performance cannot be bracketed from social action,” and not only that but we must realize that “emergent performance can transform cultural models or social structures, [and] that esthetic performance is a central arena in which gender identities and differential social power based on gender are engaged” (Sawin 2002:48). That is, gender is in many instances as much a product of discourse as it is the producer of discourse.

Fascinated by the intertwined nature of gender and discourse, we wanted to explore what contri- bution the TED talks data set (Kinnaird and Laudun 2019), previously released through this journal, could provide to understandings of language use and gender. There is always, of course, the matter of what one is called, or how one is imagined by others, and then what one calls oneself, if anything at all, and/or how that self projects itself into the world in and through language. Here, we attempt to apply two lenses to explore the role of gender within discourse as manifested in TED talks. First, we investigate the representation of speakers by gender. Second, we explore how others (by gender) are being spoken of. Put another way, we first concern ourselves with who is speaking and second how they are speaking of others and of themselves. These twinned investigations represent two (of many) di- mensions of cultural analytics: established topics of concern to the humanities and the human sciences and possible applications of machine learning to those topics, inviting either novel answers to familiar questions or new kinds of questions. Here we use techniques from supervised machine learning and statistical inference to explore the gender of the speakers. Having done that, we explore the gendering of agency in hopes of establishing a continuum across which speakers project themselves.

In the essay that follows, we begin by surveying recent work in cultural analytics that has explored the gendering of character spaces (Underwood, Bamman, and Lee 2018 and Jockers and Kiriloff 2016). Grounding our own consideration in semiotics and seizing upon the opportunity of working with spoken material, we examine how character spaces intersect with speaking subjects and subjects spoken. With our theoretical program laid out, we proceed with the difficult, and tendentious, matter of gendering the speakers of our texts, making it possible to divide the TED talks corpus into two subcorpora, one by women speakers and one by men speakers. In the third section of the essay we explore what actions are available to gendered subjects as well as how, using those subjects, we can gender verbs in such a way as to explore the continuum of gender as presented in the Is of speakers.

For this present purpose, only texts from TED main events—i.e. not TEDx or other special events—are examined. First, we extend the speaker dataset within the TED talk data to include the gender of each speaker (as can be detected from public materials). We then use these identified genders to “gender” each TED talk. We then use these gendered texts to explore agency in the TED talks and how they differ based on both the speaker’s presented gender and the gender of represented subjects within the texts. In doing so, we seek to demonstrate the value in considering gender in an exploration of a text corpora as well as to connect computational text analysis to the broader conversation about TED talks. While the TED talk corpus is small, and, as we will explore more later, unbalanced, it does offer us an opportunity to examine a well-established, and popular, discourse stream that could offer us insights into gendered subject positions in English.

By mapping out the actions available to gendered agents, principally he and she, we hope to establish a continuum within which speakers may or may not engender themselves (as the I in their speech). We are not particularly happy with the fact that the work only reflects binary gender, but the fact remains that for much of the discourse of the last few decades in which TED talks have occurred, which are themselves affected by the decades (and centuries) which preceded them, the English language has largely offered two genders. Individuals operating within such a discursive regime are largely left to their own linguistic devices to represent themselves as well as to represent others. If we can establish some baselines, we might also be able to discover interesting experiments and innovations occurring in texts in a myriad of places.

Finally, we concede that our analysis is limited in a few ways; most notably that this work only focuses on gender (as currently presented) and not other axes of diversity, including race, income status, sexual orientation, and religious background/identity. Gender was easier to study, in that we could detect a speaker’s outward presentation of gender using computational techniques on the written documentation that was available on the TED website, relying on gendered pronouns about speakers in the documentation as a signal for gender. Examining other axes of diversity would require additional data (either self-reported surveys or additional press materials with explicit references to diversity markers) on each speaker or slightly different data scraping techniques with more explicit ‘rules’ for detecting diversity (such as euphemisms or cultural synonyms). This is not to say that other axes of diversity should not be studied, but rather to explain why our analysis is not immediately transferable to other markers of diversity. Instead we see our work as a first step, one that can provide a framework for extending these kinds of questions to more areas of diversity.



Humans can’t feel wetness. There are some insects (and maybe other animals?) that can because they possess hygroreceptors. Humans do not. Our brain translates differences in temperature and pressure and converts that into “sweat rolling down” our necks. What’s fascinating about this is that our sense of temperature itself is a product of how fast the heat is being transported from our bodies.

Our bodies constantly produce heat, which means we constantly need to dump heat. If this process moves more slowly than our bodies prefer, we feel warm. If it moves far too quickly, like on cold days when we feel like the heat is “getting sucked out” of us, we feel cold.

The rate of heat transference depends upon the medium: wood feels warmer on our feet because it is slow to transfer heat. Air is even slower to transfer heat, which is why so many insulating materials—down in coats and fiber glass and rock wool batts in the walls of houses—are basically media that trap air. Ceramic transfers heat which is why ceramic tiles feel cold on your feet. (It’s also why it’s just as effective when placed above under-floor radiant heating: it’s the radiant part of the equation.) For those remembering grade school science experiments: yes, the ice cubes do melt more quickly on tile than wood floors.

Different materials have different thermal properties, so heat transfer goes at different rates depending on the material. Most of our senses are based on change in general and sometimes rates of change in particular. Our three-dimensional vision is a product of our two eyes sending different signals to our brain and our brain compositing those signals. We “get the chills” when we have a fever because our temperature is lowering more quickly than our body prefers, and we feel like we are “burning up” with fever not when the temperature is stable but as the temperature increases.

It’s also why we can see in a wide range of light levels and hear in a wide range of volume levels: our brains are very good at detecting change, and, in the case of some folk illusions, like pressing our arms to door frames and then having them feel like they are listing on their own, we can hack our brains preference for detecting differences and change for fun. It is also, sadly, how information merchants (marketers, information operators, among others) hack our brains to keep us engaged.

I want to think more about sensibility both in terms of processing information but also how it affects our relationship(s) to stories in the months ahead.

To see all posts, see the archive.

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