Chris Forster

The Reddit Classics

2026-09-02T00:00:00Z

What is a “classic” work of literature? What does Reddit think one is? When Reddit users talk about classic works of literature, which works are they talking about? I think it is worth pausing for a moment and trying to guess.

[This Space Intentionally Left Blank for Quiet Reflection, Prediction, And Preregistration of Ideas about Reddit and the meaning of “classic literature.”]

If you never spend time on the r/classicliterature subreddit, I think that what Reddit calls a classic is a little surprising. It isn’t completely shocking. The subreddit is full of discussions of well-known works of literature. But they are a sort of hodgepodge. r/classicliterature’s sense of a classic aligns imperfectly with academic notions of canonicity.

To investigate this, I counted all the works mentioned on r/classicliterature. In the course of examining them, I also considered the larger, and longer-lived, subreddit r/literature, to provide some point of contrast for what “classic” means. Of course, in so doing I realized that any serious consideration of the reading practices of Reddit and its economy of literary prestige would need to take into account subreddits like the massive r/books, and the smaller r/TrueLit. Indeed, once one starts looking, the whole matter balloons rather absurdly. A total survey would surely need to include r/RomanceBooks (more than twice the number of readers as r/literature) and r/Romantasy and r/DarkRomance, and perhaps even the smaller r/HistoricalRomance. What about r/suggestmeabook? If you perused these subreddits and were surprised by the absence of science fiction, that is probably because those discussions are collected in r/sciencefiction, r/scifi, and r/printSF. The subreddits r/Fantasy and r/FantasyBooks are likewise sizable. This list from the r/books wiki is probably the most comprehensive list of book-related subreddits I’ve seen. It includes a wide range of literature-adjacent communities, including author-specific subreddits and subreddits on book binding. Ultimately, one realizes that a serious inquiry into what Reddit considers “literature” and “classic” would require tackling all of Reddit. What seems clear, though, is that a process of self-organization has already sifted and sorted the discussion of books on Reddit in such a way that r/literature and r/classicliterature are already in tension—not only with each other but with the wider discussion of “books” and related concepts of “genre” (many of which are not confined by a particular medium). And like any online community, there have been conflicts and arguments and schisms. One detects a bit of antagonism in the very name of the r/TrueLit subreddit (which implicitly separates itself from those mere pretenders who illegitimately claim the name of lit).(I haven’t examined r/TrueLit here—though now I wish I had!) But classic does seem to have an identity. People ask about one another’s “favorite classic.” They look for advice in getting into “the classics.” So what particular works are mentioned when people on r/classicliterature talk about the classics? Let’s count ’em!

Of course, counting all the named texts is a tricky matter in its own right. I used the wonderful download tool provided by Arctic Shift. It allows one to download the archive of all posts and comments for particular subreddits on particular dates.Reddit itself, however, seems to not love its data being made so easily downloadable. One Reddit user reports being asked to remove torrents of reddit data; and another recent thread suggests that links to Arctic Shift are being removed from the platform. I downloaded all of the posts and comments for both r/literature and r/classicliterature and used an LLM to extract every named author and work of literature. I may write a separate post about that process, and offer a brief comparison of conventional Named Entity Recognition tools with an LLM option. (Spoiler: the LLM was better.)

But let’s look at the data as I have it. First, a quick overview of activity on these two subreddits.

r/literature is the older of the subreddits, starting soon after Reddit was established as a platform. While r/classicliterature has its first posts in 2016, it doesn’t really take off until around 2020. Interestingly, it grew during a period when r/literature went private (for reasons that seem to involve a disagreement among the subreddit’s moderators). Since 2024, r/classicliterature has grown rapidly and in recent months has seen more activity than r/literature.

Let’s look at the particular works of literature that are discussed in these fora, starting withthe top 100 most frequently mentioned works in these two subreddits.

These lists resonate with Melanie Walsh and Maria Antoniak’s excellent essay “The Goodreads ‘Classics’: A Computational Study of Readers, Amazon, and Crowdsourced Amateur Criticism.” Walsh and Antoniak collected works on Goodreads that were tagged classic and analyzed the associated reviews to examine how the “classics” are imagined on that platform. Walsh and Antoniak’s data provides a useful point of comparison for these two subreddits. For additional points of comparison, we can also highlight works that appeared in the Modern Library’s (infamous?) top 100 novels of the 20th century list, as well as works that Harold Bloom identified as part of the “Western Canon” in the appendix to the (infamous!) book of that title. These points of comparison help characterize what Reddit considers a classic. I’ll highlight a few things.

A Classic is Not a Poem

The top works on these subreddits are completely dominated by narrative forms, and are almost exclusively (~90% by my hand count) novels. No lyric poetry appears at all; what poetry does appear is epic narrative (Homer, the Aeneid, Paradise Lost). Whatever classic literature is, it isn’t lyric poetry. The absence of lyric poetry is just one of the ways that Reddit’s definition of literature has been narrowed so that literature becomes largely novels. I don’t think this means interest in poetry has necessarily waned. There is a rather vibrant r/poetry subreddit, comparable in size to r/classicliterature and r/literature. But the language of the “classic,” to judge from these subreddits, has attached to narrative fiction. (And this, I think, is mostly true of Walsh and Antoniak’s Goodreads data as well.)

A Classic is (Probably) Russian or Victorian

I think we can get some additional insight into these categories by comparing the works that are distinctive of each of these communities: /r/classicliterature/, r/literature, and the works tagged “classic” on Goodreads, and comparing what works appear in the top 100 of each.

If you want the titles, the diagram is a little crowded.

Works distinctive of r/classicliterature are mostly nineteenth-century fiction. r/classicliterature’s top 100 contains lots of Victorian novels: Dickens’s Bleak House, Thomas Hardy’s Jude the Obscure, Anne Brontë’s The Tenant of Wildfell Hall, Charlotte Brontë’s Villette, Wilkie Collins’s The Woman in White, and William Thackeray’s Vanity Fair. But there is also a significant helping of Russian lit: Dostoyevsky’s long short story “White Nights” and Demons and Tolstoy’s The Death of Ivan Ilyich. The other works unique to r/classicliterature’s top 100 are Thomas Mann’s The Magic Mountain, Virgil’s Aeneid, and Steinbeck’s Cannery Row, as well as Alexandre Dumas’s The Three Musketeers.

By contrast, the works in the top 100 of r/literature that don’t appear at the top of either /r/classicliterature or Goodreads tend towards the twentieth century. They include a few popular book series: Harry Potter, The Hunger Games, Dune, A Song of Ice and Fire, but also some works that are frequently discussed on Reddit (Ellis’s American Psycho, Bolaño’s 2666, Tartt’s The Secret History, Yanagihara’s A Little Life) as well as a smattering of twentieth-century novels like Pale Fire, On the Road, The Crying of Lot 49.For this analysis, I condensed all books in a series—say, Harry Potter or Dune—to a single title; that may overrepresent them, but it seemed the best compromise for my purposes.

Thirty-two works are unique to Goodreads classics.¹ Walsh and Antoniak describe a “clear influence of school systems on Goodreads users’ conceptions of the classics” (270). And certain of the Goodreads-specific texts indeed seem to reflect US public school curricula. Charlotte’s Web, The Crucible, The Giver, and The Outsiders are all unique to Goodreads, and savor distinctly of middle school English class. Reddit’s discussions seem less yoked to school readings. I’m not sure why. Perhaps discussion on Reddit is more aspirational, for finding books people want to read in the future, while readers are using Goodreads to record all the books they’ve read.

A Classic is Old

We can also get a sense of the relative historical orientation of each of these communities with a nice visualization Walsh and Antoniak offer. They plot popularity (as measured in number of Goodreads ratings) by publication year, to get a sense of how the Goodreads classics are distributed over history. I’ve reproduced their visualization, along with similar plots for r/classicliterature and r/literature. One note: because so few works in the dataset are published before 1800, I’ve limited the plot to works published after 1800.²

A Reddit classic is old. Among these three communities, r/classicliterature skews to the left, with the oldest works; Goodreads “classics” are the newest works, with r/literature in the middle.

A Classic is Canonical(ish)

Walsh and Antoniak note that “classic” is the category users rely on far more than the academic-sounding term “canon” (270), a word which, they note, hardly appears at all in Goodreads users’ reviews.³ If we take the list of texts from Bloom’s Western Canon as one measure of what constitutes the “canonical,” we can measure the canonicity of these communities by simply seeing how many works in their top 100 appear in Bloom’s list.This is a somewhat silly measure; Bloom’s list is very long. It contains more than 1,500 works, and reflects Bloom’s idiosyncratic choices: e.g., Toni Morrison’s Song of Solomon is included, but not Beloved. If Lisa Nakamura is right to note that on Goodreads “reader tastes reflect the traditional literary canon more closely than one might expect” (240; qtd. in Walsh and Antoniak 254), Goodreads nevertheless has the least overlap with Bloom’s list of these three communities. 37 of the works in Goodreads top 100 are on Bloom’s list, compared with 55 of r/literature and 66 of r/classicliterature. A Reddit classic is surprisingly well aligned to at least Bloom’s notion of canonicity.

The Classics are a Little Weird

All of which fails to account for the puzzled feeling I had reading the ranking above. Particular works often surprised. Wuthering Heights is great. But is it really number 1? And, weirder still: what is up with The Count of Monte Cristo? The Count of Monte Cristo ranks 36th on r/literature and 18th on Goodreads’s top 100 classics, but it is 4th on r/classicliterature. Have I simply missed this novel’s centrality to literary history? And, if I missed it, I am not the only one. It is absent from Bloom’s The Western Canon. The YouTube channel “Man Carrying Thing” recently highlighted the weird, and indeed recent, emergence of The Count of Monte Cristo as what he calls “gen z’s new favorite book” and the “gateway drug into classic literature for tons of people.”

One reason for the high ranking of these two works appears to be film and television adaptations.

A Bar Plot Showing the Number of Mentions of Wuthering Heights over time, spiking around February 2026, when the film adaptation was released.

As the chart above suggests, Wuthering Heights was widely discussed on r/classicliterature before the film’s release; but there is a clear spike in the month of the adaptation’s release. Indeed, the film was announced in July 2024, making the rise in posts on r/classicliterature essentially coextensive with the anticipation for Fennell’s adaptation. It is not impossible that the film alone shaped Reddit’s notion of what “classic literature” is.

A Bar Plot Showing the Number of Mentions of The Count of Monte Cristo over time, with dates of film and TV adaptations marked.

The situation is somewhat similar with The Count of Monte Cristo. While the spike is more diffuse and less well-defined, it seems that a 2024 French film adaptation, and then a British TV series (broadcast in the US in March 2026), helped support the increased discussion of Dumas’s novel. I wonder what was going on in discussions of literature on TikTok? Was TikTok getting excited about Dumas’s novel at just this moment?

If we generate Dunning Log Likelihood scores on the text of the posts associated with each of the top works in r/classicliterature we can identify terms most distinctive of each work.Ted Underwood’s post “Identifying diction that characterizes an author or genre” introduced me to the range of methods for identifying characteristic diction.

Here are the top 100 terms distinctive of posts/comments mentioning Wuthering Heights:

heathcliff bronte wh cathy catherine jane eyre nelly emily romance heathcliff’s linton hareton hindley lockwood isabella earnshaw romani edgar movie dark gothic romantic she moors abuse yorkshire race heathcliffe characters tenant gypsy pp sisters black white brontes irish austen charlotte bronte’s elordi wildfell toxic cathy’s revenge skinned hall love prejudice her fennell catherine’s frankenstein casting adaptation liverpool grange pride obsession ethnicity lintons lascar ambiguous generational trauma dialect emerald film jacob asian skin arden described robbie nelly’s story racially adaptations unlikeable thrushcross people racial withering healthcliff vocab roma margot joseph’s emily’s indian hair darker earnshaw’s relationship critics rebecca je toxicity heathcliffs

Many of the terms are character names (Heathcliff, Cathy, Nelly, Hareton, Hindley); others are key themes (romance/obsession). There are discussions of Heathcliff’s ambiguous racial identity (“romani,” “dark,” “skinned,” “ethnicity,” “ambiguous,” “irish,” and “indian”), and many seem to come from comparisons in posts about Wuthering Heights to other works (Charlotte Brontë’s Jane Eyre, Anne Brontë’s The Tenant of Wildfell Hall, Jane Austen’s Pride and Prejudice).And, of course, typos: healthcliff, heathecliff, withering. But the bolded terms, I assume, must come from discussions of the film adaptation.

I think we see a similar, though less pronounced, trend in the top 100 terms distinctive of The Count of Monte Cristo:

dumas revenge dantes edmond musketeers christo alexandre montecristo tcomc les mercedes comc bce adventure miserables abridged peace buss haydee unabridged danglars three robin pages war mis quixote prison villefort faria fernand long brothers lonesome east don punishment eden karamazov crime dove d’if critics rome areadingofmontecristo french morrel karenina vengeance hippo tulip serialized chateau hugo version century fun count’s length plot leo chocolate valentine anna miniseries abbe mc counte turner finished action comte vampa tcmc r page th benedetto paced imprisonment augustine badel nishapur classics next cheng’en edmond’s arabian blanc albert kempis claflin anycount size exeter treasure escape le slow attar

Again, there are abbreviations (CoMC, TCMC), key themes (revenge), references to editions (the Robin Buss translation; abridged vs. unabridged). The number of terms here that come from other titles (Les Miserables, The Three Musketeers, East of Eden, War and Peace, Crime and Punishment, Brothers Karamazov) makes me wonder how many of these posts are simply lists (“Top 10 Classics,” etc). But we also see discussion of the miniseries and actors like Sam Claflin and Alan Badel (from the earlier, 1964 adaptation). Anna may be Ana Girardot, who plays Mercédès, and Leo may be “Leo Harris” who plays Villefort, both from the recent TV adaptation. There are fewer words here related to adaptations than in discussions of Wuthering Heights, but I’m still willing to suggest that the reception and discussion of classics is often shaped by adaptations.

I don’t think all of the sense of miscellany I find looking at r/classicliterature‘s top 100 can be explained by adaptations. But they definitely play a part. It reshapes readers’ interests and sense of what is important, and so elevates some works in ways that are surprising. There are, no doubt, other forces shaping reader interest. Perhaps they can help explain how and why Dostoevsky has emerged as the subreddit’s most mentioned author.

Walsh and Antoniak conclude,

So what is a ‘classic’ in the twenty-first century? Based on our analysis of 144 Goodreads classics and 120,000 accompanying reviews, there are at least a few clear answers. For Goodreads and Amazon, a classic is a prominent advertising target value, a marketing tool, and a source of lucrative adaptation material. For Goodreads users, a classic is a book read in high school, a book that inspired a TV show, or a book that other Goodreads users have tagged as a classic. (Walsh and Antoniak 276)

The works of r/classicliterature have a lot in common with this description of Goodreads classics. They are influenced by adaptation in a similar way. They appear less indebted to high school curricula. They are, if anything, more canonical than those of Goodreads; and they definitely tend to be older. But any appeal to the classic is an attempt to claim the cultural capital invested in the idea of literature and literary reading. Online, at this moment, such a claim occurs at a moment when literature’s value and prestige are called into question.

In that environment, the pastness and even out of stepness that are ascribed to the “classics” by r/classicliterature is essential. In reimagining a collection of texts as “classics,” the subreddit establishes a “selective tradition,” defined implicitly against a dominant culture. Raymond Williams’s description of the resistant power of residual meanings and values is reflected in the attempt to establish “the classic” as something at odds with the dominant media enviroment.

at certain points the dominant culture cannot allow too much residual experience and practice outside itself, at least without risk. It is in the incorporation of the actively residual—by reinterpretation, dilution, projection, discriminating inclusion and exclusion—that the work of the selective tradition is especially evident. This is very notable in the case of versions of ‘the literary tradition’, passing through selective versions of the character of literature to connecting and incorporated definitions of what literature now is and should be. This is one among several crucial areas, since it is in some alternative or even oppositional versions of what literature is (has been) and what literary experience (and in one common derivation, other significant experience) is and must be, that, against the pressures of incorporation, actively residual meanings and values are sustained. (Williams 123)

r/classicliterature is a selective version of the literary tradition. To a literary historian the absence of lyric poetry from its account of the “classics” is surprising; as, I think, are the particular works it elevates. But if we understand the classics as a reaction formation to social media it may be less so. A classic is something from a time before and, most crucially, a time before social media, and short-form social media (like TikTok, Reels, and Twitter) in particular. Without being flip, I think The Count of Monte Cristo ends up as one of the key “classics” because it is so long, and that signals that it is not TikTok or Twitter.

The Reddit classic is just one interpretation of the place of literature in the media environment of the present. Different, less antagonistic ways of imagining the relationship between social media and literature exist. After all, lyric poetry—so notably absent from Reddit’s classics—is short-form media avant la lettre. It certainly appears like the literary form best suited to the era of TikTok and Instagram reels. And, in some ways, it thrives there. “Instapoetry” too is a “selective version” of literature. In that version, however, poetry is not in antagonistic relationship to social media. Appeals to the idea of the “classic” are far less present there. Instead, in Rupi Kaur (only the clearest example of a reconciliation of poetry and social media), value manifests not as “classic” but as something like emotional earnestness.⁴

r/classicliterature expresses a vision of literature and literary value, I am suggesting, that is meaningfully opposed to the dominant media ecology. It is opposed to social media in a way that Instapoetry cannot be. But in so valorizing the old, it risks thoughtlessly importing a very conservative vision of what literature is. Walsh and Antoniak note that “the Goodreads classics are considerably less diverse in terms of race and ethnicity of their authors” (254). The top 100 of r/classicliterature is even less ethnically diverse and, despite Emily Brontë taking the top spot, it is pretty male (as evident in the mentions vs. publication date plots above, which are color-coded by author gender).

So a final, possible definition. A Reddit classic is a work in antagonistic relationship to the media ecology of the present. That antagonism is not absolute (one signals one’s devotion to the classic on social media after all), but the idea of the classic as old and long reshapes ideas of canonicity to stress a difference from contemporary social media. The Reddit classic is an instance, in Williams’s words, of imagining “alternative or even oppositional versions of what literature is.” Here, I am suggesting, that opposition is implicitly to the wider media ecology where the values we conventionally associate with literature are receding into the past. Under such a definition, a book, like The Count of Monte Cristo, that may not be traditionally canonical, emerges as a classic. But this reshaping and reimagining of what literature is, while alternative and oppositional in some regards, is not without problems of its own.

Notes

If you’re interested in the data behind these plots, I’ve made a digest of the works mentioned available in a zip.

Per my habit recently, LLMs were used for data extraction and for generating the code behind some of the plots. The prose, however, is all mine.

Sept. 7 Update: After publishing the first version of this post, I noticed an error. Works that have a number in their title were sometimes undercounted by not merging different versions of the titles. Thus, redditors would refer to both 1984 and Nineteen Eighty-Four, and 100 Years of Solitude and One Hundred Years of Solitude. I used LLMs to find all such instances and merge them. The only affect in the top 100 rankings were on those two titles already mentioned. The new versions are above.

Works Cited

Cole, Teju. “"I Don’t Normally Do This Kind of Thing": 45 Small Fates.” The New Inquiry, 13 Aug. 2013, https://thenewinquiry.com/blog/i-dont-normally-do-this-kind-of-thing-45-small-fates/.

---. “Seven Short Stories about Drones.” The New Inquiry, 14 Jan. 2013, https://thenewinquiry.com/blog/seven-short-stories-about-drones/.

Egan, Jennifer. “‘Black Box’.” The New Yorker, 28 May 2012, https://www.newyorker.com/magazine/2012/06/04/black-box.

Nakamura, Lisa. “‘Words with Friends’: Socially Networked Reading on Goodreads.” PMLA, vol. 128, no. 1, Jan. 2013, pp. 238–43, https://doi.org/10.1632/pmla.2013.128.1.238.

Walsh, Melanie, and Maria Antoniak. “The Goodreads ‘Classics’: A Computational Study of Readers, Amazon, and Crowdsourced Amateur Criticism.” Journal of Cultural Analytics, vol. 6, no. 2, Apr. 2021, https://doi.org/10.22148/001c.22221.

Williams, Raymond. Marxism and Literature. Oxford University press, 1992.

They are: “The Adventures of Tom Sawyer” “Alice’s Adventures in Wonderland & Through the Looking-Glass” “Call of the Wild” “Charlotte’s Web” “The Chronicles of Narnia” “A Clockwork Orange” “The Color Purple” “The Crucible” “The Diary of a Young Girl” “The Giver” “Gone with the Wind” “Gulliver’s Travels” “The Hunchback of Notre Dame” “The Importance of Being Earnest” “The Little Prince” “A Little Princess” “Mansfield Park” “A Midsummer Night’s Dream” “Much Ado About Nothing” “Northanger Abbey” “One Flew Over the Cuckoo’s Nest” “Othello” “The Outsiders” “Peter Pan” “The Phantom of the Opera” “Robinson Crusoe” “The Secret Garden” “Strange Case of Dr Jekyll and Mr Hyde” “The Time Machine” “A Tree Grows in Brooklyn” “Uncle Tom’s Cabin” “The Wonderful Wizard of Oz”.↩︎
For r/classicliterature there are eleven excluded works: the Iliad, Odyssey, Don Quixote, Divine Comedy, Paradise Lost, Hamlet, Romeo and Juliet, Aeneid, Candide, Macbeth and the Bible; r/literature has those eleven, plus Beowulf in its top 100—so r/literature has slightly more pre-1800 works, just fewer nineteenth-century works.↩︎
Walsh and Antoniak write, “more than 15,000 Goodreads reviews explicitly mentioned the words ‘classic’ or ‘classics,’ while just under 400 reviews mentioned the words ‘canon’ or ‘canonical.’ This simple metric reveals a clear fault line in literary critical discourse between scholars and readers” (Walsh and Antoniak 270).↩︎
Other experiments with Twitter literature (including those by Teju Cole and Jennifer Egan) would offer another fusion of literature and social media outside the logic of the classic, and in very different terms Egan.↩︎

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Assays of Bias: LLMs and Geometries of Meaning

2026-07-12T00:00:00Z

There are plenty of things to say about Large Language Models and the discourse around AI. I may not be able to resist contributing to that discourse. But this post (and probably some others to follow it up) is an attempt to step away from the Discourse, and just try to better understand this technology.

That choice itself can be reasonably critiqued. Can we afford to look away from the ways that these technologies exacerbate climate collapse and prop up hypercapitalist totalitarianism? Can one use a technology that is largely controlled by people who seem somewhere between comfortable and downright eager to placate the current US presidential administration? Besides, how interesting can a tool be that is undermining education and facilitating illiteracy and cheating? What could conceivably be learned from a technology that is little more than an act of mass intellectual property theft? A technology that is an insult to the very idea of creativity? To the very notion of humanity? Can it possibly be worth scrutinizing the details of the valve timings on an engine that is shredding the already-frayed fabric of collective life?I don’t think all these critiques are equally reasonable though.

My modest, tentative answer is: yes, it is worth it to better understand these things. In part this is because I don’t fully agree with all the critiques (perhaps unfairly) summarized above. One can be curious without being uncritical. And most fundamentally, what I think I don’t share with AI skeptics, is utter shock that these machines work as well as they do. It is very hard for me not to remain surprised that a next-token predictor can do the things that it does. If you had told me ten years ago that a technology built by digesting every book and reddit post would be able to pass the Turing test, I would have been deeply skeptical—and it is my sense that most other people would have been too. It was simply not obvious that this technology could produce these results. The shape of the political moment and the massive investment in these technologies (and those are not uncorrelated forces) have produced strongly polarized responses, with accordingly stark utopian/dystopian visions. But I remain surprised, and curious, how software, trained this way, produces these outputs.

Below, I want to talk about a more specific instance of that surprise concerning how LLMs represent individual tokens. That realization can be summarized: the “meaning” of each token as represented in an LLM is unique to its context. The phrases “I saw a bear in the woods” and “a bear was running through the woods,” will use the same token (“bear”) with the same token ID. They may be describing the same particular bear. But the context reshapes the representation of that word so that the token “bear” will have a unique value, or meaning, in these two cases. Equating the value of some vector—some list of numbers—with the “meaning” of the term is tricky. Another way to say this: meanings, in an LLM, are not points, they’re regions. In one way, this is just repeating the premise of contextual word vectors. But it still struck me as odd. So I wanted to try to walk through it carefully. This post is my attempt to do so.

Another Attempt to Explain how LLMs Work

Understanding my point requires trying to understand how LLMs work. I’ve found different explanations of LLMs stress different elements of the machinery. This one will try to focus on how vector representations of tokens are reshaped. Recall, that the first step when an LLM processes text (I’ll talk about inference, and ignore training here) is to split the input into terms from the model’s vocabulary—to tokenize the input. After the text is tokenized, each token is replaced with an initial vector of some high dimensionality. For gpt2-small (the smallest version of the gpt2 model, and the one I’ll be using as an example), those vectors have 768 numeric values. That means each token can now be imagined as a point in 768-dimensional space. For larger open models, that dimensionality can be larger. The embedding vector dimensionality for the most current models from Google, OpenAI, Anthropic, and others is not publicly disclosed. Whatever its size, this initial “static” embedding is basically the same as the word embeddings of word2vec or GloVe. LLMs learn this embedding during model training. I think this initial embedding value can be loosely described as the average meaning of a token across the training data. After this vector look up, all tokens of the same type (i.e. having the same ID), will start with the same value. However, even at this first stage, positional information is added to the vector. This “positional information” is not just an index of the position itself, but another vector, of the same dimensionality as the embedding, which is, like the initial embedding, learned during training.

Consider the text input: “dog dog.” That looks like two instances of the token (dog).It could of course be the token “dog” and the token ” dog,” but we’ll ignore that. Even in this first stage all tokens of the same type (ID) will probably not have the same vector value, because to a simple token value is added “positional information.” This though is the trivial way that token values are essentially unique.

The work of the LLM is to then reshape this vector based on context, so that the “best” possible next token can be predicted. To do so, all those vectors values (that have been looked up and altered by position vectors) are passed through a series of “layers.” A layer is simply a set of mathematical functions to reshape the vector values. gpt-2 has 12 layers. (Again, details like “number of layers” are not typically disclosed for the chatbots most folks think of as LLMs: ChatGPT, Claude, etc.) The neural network alters each vector based on all the preceding tokens in the context window. Knowing exactly which of those preceding tokens should influence any particular token, and how (how many, and which of those 768 numbers to change; and how to change them—increase or decrease), is of course non-trivial. It’s the whole (decidedly black-box-y) mechanism. This is the “attention” mechanism at work: a system of keys, values, and queries that is the backbone of transformer-based LLMs. The most relevant terms in the context push or pull that initial, layer-0 vector value in different directions. And then you do it again… and again, for each layer in the model (along with some simpler “feed forward” layers in between). I struggle to have a meaningful, intuitive understanding of this process; and I suspect that most other people do too.(I, like literally millions of others, have found 3Blue1Brown’s videos illuminating—on LLMs and much else besides.) But ultimately, however baroquely complex, this is a matter of lots of cross products passing through some non-linear functions, with the effect of reshaping each vector according to values learned from the training data. That training process (frustratingly called “pretraining”) has “learned” the particular values that will reshape the input vectors in ways that are meaningful. Or, perhaps more accurately, values that will reshape the input vectors to be most consistent with the training data. (Keep in mind: the task during training is accurately predicting the next token given the input.)This has led to the description of LLMs as “autocomplete on steroids,” a description that helpfully gives one an intuition for the main goal of LLM training, but may underplay the particular means of how they pursue this task.

So, text becomes tokens; tokens becomes vectors; and the vector values change based on the other vectors in the context window (or, more precisely, the preceding vectors). And all these values, in all these vectors, are changing in parallel. The “product” or “goal” of this process, in typical use, is to predict the next token after the last token in the context window. Doing so involves reshaping all the vectors, multiple times. The autoregressive character of LLMs means that all vectors representing the context window reshape themselves in light of all the preceding vectors. That this process could write even a bad poem or produce an inaccurate summary of the Krebs cycle still strikes me as deeply weird.

After all the tokens have been reshaped across all the layers, but before the next token is generated, we have a reshaped set of vectors that all been changed based on the values/weights/parameters learned during training. How do we get from these values to the next token or word? We take our reshaped final vector and turn it back into a token by “unembedding” it. As the name suggests, this is essentially the reverse of the embedding stage. In gpt2, it is done by comparing our final 768-dimension vector to the initial vectors representing each token in the vocabulary. (Whether the “frontier” models use the same matrix for embedding and unembedding is another undisclosed detail.) This comparison is done with that staple of linear algebra, the dot product. One dot product is calculated between the final vector and a vector representing each term in the vocabulary. (That is a lot of dot products—tens of thousands at least.) Dot products with higher numeric values mean greater similarity. (These numbers, to the frustration of neophytes and nonexperts, are called logits—despite having nothing to do with logistic regression or with the log-odds.) Using softmax, this list of numbers can easily be turned into a probability distribution across the whole vocabulary of the model.Or, you could use a setting like “temperature” to reshape this distribution, and so likelihood of tokens being sampled (about which, more below). In typical inference (when you’re using a chatbot like Claude or ChatGPT), there is complicated (and stochastic) logic to select a token from this distribution. In practice, always choosing the top “most likely” next token produces bad outputs. And so, there is a deliberate injection of randomness to what is otherwise a deterministic processes built on a probabilistic representation of the training data.Technically, this determinism can be lost just through matrix multiplication because of the way that floating point rounding works in digital computers. If LLMs are stochastic parrots, they are stochastic in two ways: their training attempts to model the conditional probabilities of a distribution that is only very loosely sampled by even their enormous training data; but also, the text they generate from these distributions involves deliberately injecting randomness because, empirically, it makes the output “better.”¹

However it is selected, the new token is added onto the input. If this selected token isn’t some special “end of response” token, the whole thing starts over, now with that one added token that will, in turn, reshapes the values that come after it in the window. And you do the whole thing again.

You might note that only the last token of the last layer is unembedded in order to predict the next token. So why did we do all those other calculations—for each token at each layer? This is the autogressive character of the LLM: in order to get that last token’s vector representation, we need all the others, and each of those need all the layers, all reshaped as if they were trying to predict the next token. But each token in the context window is being reshaped.² So that is my own, overlong, explanation of LLMs.

Each Meaning is Utterly Unique

After struggling to understand this process, I was surprised to notice that the value of any contextualized vector is almost certainly unique. I don’t know that this is counterintuitive exactly, because I have no intuitions about how meanings represented as points in high-dimensional space. The naive sense that the word “jello” is the same word in every sentence is belied by a system which rearranges its representation of “jello” in every appearance. Context reshapes meaning, so that the representation of any token within that message is unique to the message.

Should that be surprising? After all, every unique sentence is describing, or doing, something unique. Doesn’t my point reduce to the rather obvious observation that unique sentences are unique? Maybe! I think my surprise stems from a naive sense of the discreteness of words. There is a tension between the abstraction that language requires by carving the world up into manipulable meaning-units like words, and any specific use of it. I am ready to concede that “unique sentences are unique.” I was surprised to realize that in an LLM every unique sentence (or unique context) is made up of unique vectors.

An LLM enacts this tension between the abstract and the particular, between the token-type and the contextualized, vectorized token value, in a particular way. After the initial embeddings, the LLM simply is a series of vectors in a continuous high-dimensional space such that each token-representation is unique. To find two vectors that are identical in any position (other than the first!) seems impossibly unlikely. Because computers represent things in digital bits, the space is not truly continuous. But consider: each token position in gpt2 (a “small” “large language model”) is represented by 768 32-bit precision floating point numbers. That means each of those 768 numbers can take on one of 2³² values. 768 numbers, each capable of 2³² unique values, means 2^(32*768), or 2^24,576 unique combinations. That is so many possible values. So many, that if you, try to print the number, Python returns a ValueError.³

My point is simply that the in an LLM is no longer what I naively assume “a word” is. To say, as LLM explainers often do, that in an LLM each word is replaced with a set of numbers is not untrue. But the description is too coarse to be useful. More useful is thinking about how words are first mapped in, and then back out, of the model’s high-dimensional space. In this model words and meanings are less discrete points than neighborhoods in the high-dimensional space that is constructed by the LLM and its training. Even though all the tokens will have the same token ID, and will begin with the same initial embedding (even though modified by their position in the context window)—everything else will be different. Those vectors will be moved around, so when the is output, it is because the predicted vector has ended up in some region or neighborhood of high-dimensional space, near to other thes, such that it unembeds as the. A vector becomes a word in an LLM not because of some essential quality, but simply because that token-representation is the closest one based on a dot product. This idea, that meaning is ultimately simply a matter of proximity (of near and far) is just very strange.

How Grover Interprets Embeddings.

Consider this very basic illustration. I fed each of the following sentences into gpt2 independently (i.e. the model only ever saw one line from this passage at a time).

I went to the bank.
Joe went to the bank.
I went to the store.
Joe went to the store.
I am going to the store.
I will eat the elephant.
Green ideas sleep furiously in the dull mortgage.

I40 went1816 to284 the262 bank3331 .13

Joe19585 went1816 to284 the262 bank3331 .13

I40 went1816 to284 the262 store3650 .13

Joe19585 went1816 to284 the262 store3650 .13

I40 am716 going1016 to284 the262 store3650 .13

I40 will481 eat4483 the262 elephant20950 .13

Green13719 ideas4213 sleep3993 furiously44896 in287 the262 dull19222 mortgage13682 .13

Each of these sentences was independently tokenized, replaced with a set of 768-dimension vectors (one per token), and then processed, layer by layer. At each layer, the weights that the model learned during training dictate how each of these vectors modify each of the other in each layer. Now imagine stopping the machine, opening it up mid-processing, and looking at those lists of numbers. This sort of inspection has been done in a more rigorous way by serious researchers. But I grabbed a layer in the middle (layer 6, of this 12 layer network). From this layer I get one vector per token for each sentence. I then used Principal Component Analysis (which I think of as the most boring, straightforward method of dimensionality reduction) to reduce the 768-dimensional vectors for each term to 2 dimensions and created the plot below.There was an additional element of normalization because otherwise GPT2’s layers behave oddly. I only learned that in the course of trying to produce this plot. While the typical use of gpt2 would be to predict the next token, I haven’t retrieved that. For this illustration, it doesn’t matter.

Each point in the plot above is represents a token from one of the above listed sentences (hover over to see the sentence, or sentences, from which it was drawn). I’ve color coded the most frequent words here, to show how in the LLM even the “same” token is mapped to different places within the model’s space. Consider the word/token the above, from the sentences “I went to the store” and “Joe went to the store.” These two sentences feature the same token, in the same position, with a meaning that is as similar as I could imagine. But the vectors representing the in these two sentences are still separated a little. On this 2-dimensional reduction, they’re close. Most instances of the are in the lower right of this plot, representing sentences like “I am going to the store” and “Joe went to the bank.” But some end up in the top left. The the of “I will eat the elephant” is closer to ideas and sleep than the the of “I went to the bank.” I often see plots like these stress the proximity—the nearness of similar terms. We normally plot a bunch of terms and note how semantically similar terms appear near each other, but far from dissimilar terms. Just as important as the nearness of similar terms is the distance a model creates between similar, and even “identical,” tokens. Note, though, that the values for the in “I went to the store” and “I went to the bank” will be identical. That’s because all the tokens and positions to the left are identical, the “context” is identical. (The plot represents identical vector representations with a larger point.) This again is because of “predict the next token” task. When you’re predicting the next token, then “I went to the bank” and “I went to the store” are identical right up until the last token (bank/store) (in a “bidirectional” model, like BERT, this would not be the case.)

This is a toy example. gpt2 is a very small model. Reducing 768 dimensions to 2, for purposes of plotting is obviously a lossy and potentially misleading process.These components represent only ~25% of the total variance, which is suprisingly good given that we started with 768 dimensions! I wouldn’t “read” too much into this plot. It captures a complexity in thinking of meaning as space. If meaning is space, words are not simply points, but regions. And if that’s the case, the sort of questions we might ask change. Are different regions larger? Is shape meaningful? Semantic space can be described not simply in terms of near and far, but with a richer vocabulary.

The Well-Wrought Vector: Uniqueness and Precision

Reflecting on this way of imagining meaning, I was reminded of a passage from Cleanth Brooks’s The Well Wrought Urn (1947). For Brooks, poetry is a mode of language where each meaning becomes as precise and utterly individual as a contextualized vector. Yet,how this uniquness works is rather different. Language, for Brooks, tends to drift into abstraction and imprecision. It is a flawed tool. But this drift, in poetry, can be beaten back and precision achieved. In trying to describe this model of language, and poetry’s place in it, Brooks relies on images of meaning as space. Discussing Wordsworth’s “It is a beauteous evening, calm and free”, Brooks writes:

T. S. Eliot has commented upon “that perpetual slight alteration of language, words perpetually juxtaposed in new and sudden combinations,” which occurs in poetry. It is perpetual; it cannot be kept out of the poem; it can only be directed and controlled. The tendency of science is necessarily to stabilize terms, to freeze them into strict denotations; the poet’s tendency is by contrast disruptive. The terms are continually modifying each other, and thus violating their dictionary meanings. To take a very simple example, consider the adjectives in the first lines of Wordsworth’s evening sonnet: beauteous, calm, free, holy, quiet, breathless. The juxtapositions are hardly startling; and yet notice this: the evening is like a nun breathless with adoration. The adjective “breathless” suggests tremendous excitement; and yet the evening is not only quiet but calm. There is no final contradiction, to be sure; it is that kind of calm and that kind of excitement, and the two states may well occur together. But the poet has no one term. Even if he had a polysyllabic technical term, the term would not provide the solution for his problem. He must work by contradiction and qualification.

We may approach the problem in this way: the poet has to work by analogies. All of the subtler states of emotion, as I. A. Richards has pointed out, necessarily demand metaphor for their expression. The poet must work by analogies, but the metaphors do not lie in the same plane or fit neatly edge to edge. There is a continual tilting of the planes; necessary overlappings, discrepancies, contradictions. Even the most direct and simple poet is forced into paradoxes far more often than we think, if we are sufficiently alive to what he is doing. (9–10)

Brooks’s description helps me understand my own surprise at this element of contextual embeddings’ behavior. “The terms are continually modifying each other, and thus violating their dictionary meanings.” This contrast between the model of meaning as space and the dictionary is clarifying. The dictionary is organized by words. It imagines a meaning as an instance of some general type. Contextual embeddings start with a dictionary-like look up; but the whole process that makes them contextual embeddings involves modifying and altering them in a series of ways such that the vector representing a token may no longer most resemble the token it began as. We turn to the dictionary believing that we can understand a word in isolation. But the fantasy of the self-contained integrity of a word, embodied by the dictionary, is violated by the power of a context, which “continually modifies” the individual word. Science (Brooks suggests) attempts to solidify the mushiness of language, freezing it into a stricter denotation. It intensifies the logic of the dictionary, adding more and more words. It introduces new terms, and uses old terms more strictly. Poetry achieves a greater precision. This is a deliberately audacious claim—outperforming science on its own territory. Poetry does accepts the imprecision language, but overcomes it through paradox. By bracing one imprecise meaning against another. Poetry navigates meaning-space more precisely not by trying to finely indicate a particular point, but by understanding precise meaning as an intersection in meaning space.

Brooks’s view of a meaning is LLM-like insofar as he imagines words as imprecise regions, representable as spatial, geometric forces. A desired meaning and its expression don’t line up; you use a metaphor, but that comparison includes too much or not enough—the meanings “not lie in the same plane or fit neatly edge to edge.” Meaning is “a continual tilting of planes”—a nice enough image for meaning as space. Brooks further elaborates this geometric model of meaning, comparing the poet’s work to a quotation from Hamlet (Polonius’s advice on successful spying).

with assays of bias
By indirections find directions out. (Hamlet II.1)

Brooks explains:

Shakespeare had in mind the game of lawnbowls in which the bowl is distorted, a distortion which allows the skillful player to bowl a curve. To elaborate the figure, science makes use of the perfect sphere and its attack can be direct. The method of art can, I believe, never be direct—is always indirect. But that does not mean that the master of the game cannot place the bowl where he wants it. (10)

Science seeks to reform and rationalize meaning space, to straighten it out, to allow precise statement. The poet instead takes advantage of this curvature through paradox.

LLMs are not poets. They might, as Leif Weatherby suggests in Language Machines (which is undoubtedly the best, and most sophisticated work, on the intersection of structuralist theory and LLMs), represent something like Roman Jakobson’s “general poetic function” ( esp. chapter 5). But I want to end by observing a difference between LLMs and Brooks’s description. They share a geometric understanding of meaning, and focus on individual meanings a unique points within this geometric space. Yet, the fact that each message/context is singularly unique is not the same as Brooks’s assertion that the poet’s meanings are singularly precise.(Crucially Brooks completely separates poetry—and perhaps all of “literature”—from the rest of language and culture. The rejection of this segregation is probably the central fact of academic literary criticism since the late 1960s.) At the crucial moment, Brooks must reach outside language, through a deictic gesture. To follow the reading I’ve quoted above, we must agree with him that the paradox of the feeling Wordsworth describes exists. We must recognize “that kind of calm and that kind of excitement.” Brooks relies on this ostensive, deictic appeal to link the uniqueness of the poetic representation back to the world. And it is this link that allows the uniqueness to indicate not a point in semantic space, but a point in the world. For Brooks, the uniqueness of the representation allows it to overcome the imprecision of language and name something exact in the world. In an LLM, of course, uniqueness reflects nothing except consistency with training data.

Discosure: All the prose here was, for better or worse, written by me. The code I used to generate the plot above was generated by Claude Code.

Works Cited

Brooks, Cleanth. The Well Wrought Urn: Studies in the Structure of Poetry. Harcourt, Brace & World, 1947.

Holtzman, Ari, et al. The Curious Case of Neural Text Degeneration. 14 Feb. 2020, https://doi.org/10.48550/arXiv.1904.09751.

Weatherby, Leif. Language Machines: Cultural AI and the End of Remainder Humanism. University of Minnesota Press, 2025.

This fact is really quite weird! Always picking the most likely most next token produces bad output. Human generated text, when measured, doesn’t tend to predict the most likely term. This suggests a tension between the training goal of LLMs and the reality of actually existing text. The following is the most interesting observation on this peculiarity that I’ve read: “Why is human-written text not the most probable text? We conjecture that this is an intrinsic property of human language. Language models that assign probabilities one word at a time without a global model of the text will have trouble capturing this effect. Grice’s Maxims of Communication (Grice, 1975) show that people optimize against stating the obvious. Thus, making every word as predictable as possible will be disfavored. This makes solving the problem simply by training larger models or improving neural architectures using standard per-word learning objectives unlikely: such models are forced to favor the lowest common denominator, rather than informative language” (Holtzman et al. 7).↩︎
You could examine those tokens to try to inspect what is going on—this is the idea of the “logit lens,” which emerged soon after gpt2 to try to understand what those other layers are doing.↩︎
But if you insist. Each vector takes on one of 1,297,697,410,448,600,346,538,135,939,031,222,035,519,728,998,700,232,084,778,543,406,238,891,850,334,076,587,129,694,423,527,303,789,473,987,996,649,748,036,250,082,317,297,862,768,096,218,198,670,173,239,042,717,243,022,404,097,405,358,815,383,304,467,520,506,016,644,412,538,620,827,664,539,590,981,133,764,939,559,889,792,881,228,022,688,086,387,013,159,779,474,659,377,228,904,660,169,451,046,977,211,569,734,880,854,126,915,463,239,386,217,836,496,696,394,119,476,173,179,833,969,555,344,970,609,797,275,119,509,074,848,382,649,401,987,542,136,420,071,348,640,341,362,674,535,922,153,064,214,928,764,822,074,773,723,105,483,968,708,166,268,391,770,001,008,193,546,523,866,907,057,414,757,404,671,950,237,688,337,348,889,336,470,255,095,022,937,678,634,206,826,776,953,401,707,839,620,349,125,443,294,562,166,015,762,041,130,046,137,078,304,507,285,578,022,169,570,112,201,489,325,577,731,123,168,662,277,782,774,175,060,360,171,925,158,129,387,447,235,460,016,960,647,395,805,695,215,807,380,983,699,032,255,008,369,041,788,431,642,510,192,930,958,332,314,819,211,877,161,067,761,083,492,745,514,752,985,497,572,005,591,098,300,898,501,362,642,140,331,600,815,207,942,822,100,023,617,638,152,801,392,095,870,047,020,586,430,664,761,145,703,707,938,535,906,464,406,225,255,478,970,566,082,524,079,758,186,098,468,463,624,073,204,527,508,260,284,048,962,987,191,501,560,528,120,556,297,523,750,493,275,728,157,552,504,092,216,062,654,422,454,903,614,664,854,707,523,432,470,828,142,420,198,725,979,347,441,441,153,726,933,242,354,091,395,389,601,490,357,550,432,526,686,664,921,480,152,112,368,216,565,882,267,653,305,092,881,044,402,210,015,692,351,775,891,368,301,146,414,351,981,968,923,350,373,884,448,126,606,724,415,090,942,918,312,718,285,342,778,976,990,207,729,827,271,369,182,128,651,357,747,021,196,541,162,375,775,864,442,262,272,153,790,454,445,842,771,953,191,553,371,532,285,370,457,313,533,801,325,474,998,460,876,129,865,738,698,744,711,785,842,767,536,060,631,433,138,044,176,622,498,313,429,867,186,433,040,976,399,943,263,724,380,738,286,727,261,473,007,118,657,345,412,394,415,247,329,923,139,297,422,468,472,074,973,105,376,268,973,641,269,265,766,763,550,078,055,034,204,797,006,653,822,418,032,652,646,514,526,001,315,721,600,841,383,643,905,592,770,018,559,411,242,071,334,524,097,130,309,230,341,753,863,086,433,457,304,332,201,258,143,789,693,310,794,050,368,139,526,486,425,810,515,920,526,690,514,966,426,657,735,297,009,822,822,347,308,821,059,461,601,928,047,577,404,088,595,982,353,448,715,973,318,764,903,612,555,984,878,557,394,689,727,581,475,898,934,959,903,510,650,539,523,742,387,358,493,028,687,147,999,727,868,782,905,595,440,800,670,060,908,385,225,123,633,807,300,055,026,747,539,139,596,139,699,572,013,416,469,479,147,050,202,026,035,823,595,658,241,564,260,446,273,117,491,733,149,544,139,183,746,349,729,775,131,574,234,475,326,952,793,240,708,302,248,933,142,582,357,669,936,354,521,381,623,946,955,024,586,401,006,691,321,883,101,748,742,366,234,089,670,835,411,314,692,531,612,234,047,518,081,809,098,799,701,912,741,535,499,566,760,826,901,138,816,147,745,050,571,154,414,816,928,387,311,642,876,344,456,295,168,525,833,275,257,389,289,089,002,855,575,634,203,975,416,381,552,717,491,969,566,299,428,479,451,557,318,976,731,232,361,052,347,688,093,782,450,759,172,496,613,457,785,426,674,586,796,341,558,349,801,020,810,439,696,683,918,494,133,116,675,377,012,157,259,822,338,829,144,906,520,875,137,659,210,690,851,412,992,747,336,492,326,914,326,688,736,069,877,775,771,817,439,628,583,041,620,269,827,979,325,221,988,483,150,742,648,764,196,519,796,064,219,048,218,581,790,238,796,107,433,591,494,903,767,305,623,499,779,161,623,829,491,394,153,438,577,602,753,296,015,872,912,403,939,133,781,995,669,305,118,750,402,512,528,595,977,334,351,459,763,436,583,313,632,905,069,902,953,475,008,068,913,625,365,490,282,617,160,080,393,262,801,172,330,313,146,148,236,745,449,921,164,948,060,806,911,452,069,815,451,549,857,673,708,623,443,802,874,549,340,502,789,744,414,566,660,268,553,543,566,910,755,632,036,051,772,333,652,210,319,136,753,402,028,349,537,857,280,475,205,705,938,845,645,749,930,586,645,051,732,086,272,786,583,983,061,014,900,742,914,501,299,157,138,806,769,790,024,767,722,750,633,585,764,520,182,070,926,356,100,664,138,850,210,715,705,598,842,122,905,532,688,122,285,662,818,997,869,709,358,741,389,382,818,551,074,714,596,490,081,150,107,374,227,933,685,329,986,845,487,543,930,253,675,616,739,882,062,385,836,491,209,598,753,928,114,457,207,376,576,356,255,107,028,642,034,175,437,122,941,195,648,806,083,085,549,982,368,852,278,066,886,136,087,399,156,800,246,270,140,823,155,995,306,361,156,851,746,204,728,297,233,434,496,857,940,379,371,424,957,968,305,685,415,799,097,530,416,332,358,418,536,606,933,116,174,315,215,327,097,663,282,370,339,032,753,068,632,038,435,102,853,566,876,821,617,976,116,764,083,041,289,405,510,235,702,138,673,206,084,233,412,415,977,601,761,552,494,634,402,751,172,069,930,297,017,193,063,865,055,410,231,671,763,544,260,826,996,010,519,413,376,980,317,677,626,223,193,586,838,527,728,173,723,646,908,915,741,191,050,522,407,467,628,834,478,108,728,633,562,456,189,294,949,972,907,072,217,835,516,032,768,065,884,130,726,042,757,123,496,466,996,641,424,573,962,320,966,764,049,957,703,899,302,070,352,183,284,617,821,442,035,886,042,565,424,815,190,185,607,850,336,275,339,789,767,440,541,239,244,969,508,140,362,837,460,506,154,194,733,006,766,882,061,115,814,920,468,876,280,572,493,497,117,209,685,427,909,368,149,860,795,633,997,946,052,586,776,078,940,905,667,705,642,198,211,724,479,972,324,896,764,242,258,402,262,954,862,503,531,240,323,448,374,982,115,808,740,282,888,904,588,259,700,595,206,248,104,595,240,127,900,801,503,490,967,825,067,529,653,117,701,159,787,653,102,842,260,359,451,835,029,603,941,483,333,188,517,120,844,494,235,705,765,414,044,718,522,420,703,999,784,968,967,892,709,894,636,845,967,827,768,197,323,409,343,104,494,220,034,948,026,747,051,910,930,855,371,819,909,486,828,996,182,563,646,818,626,752,659,938,545,654,081,089,955,366,580,546,278,471,380,839,211,695,493,074,974,418,621,269,683,720,940,027,491,064,776,543,824,006,365,637,843,286,379,190,389,074,049,228,943,087,667,090,419,445,854,074,062,458,815,715,066,335,645,273,655,551,762,071,864,833,938,653,812,714,755,489,303,912,189,289,249,791,427,033,572,312,255,750,132,692,158,245,570,011,930,244,438,550,785,983,827,405,238,706,612,842,402,776,718,110,713,673,430,107,593,004,180,336,747,715,384,598,315,488,260,995,655,046,945,837,985,356,787,156,536,989,126,867,235,288,282,769,656,063,048,530,249,588,946,471,234,472,131,973,601,315,870,490,469,871,433,260,386,093,197,064,710,657,885,805,829,743,893,244,275,588,028,819,074,466,906,030,714,862,761,545,813,522,171,421,020,133,884,168,241,983,512,478,748,442,083,201,937,260,172,820,273,409,005,526,650,193,325,470,249,073,091,805,786,606,060,858,378,895,109,345,350,695,626,788,146,217,788,690,663,147,695,114,726,616,794,137,652,420,096,127,458,684,110,437,426,367,896,052,974,371,764,756,234,020,770,269,534,434,111,728,507,595,798,614,972,033,157,716,166,985,275,365,617,494,263,241,070,561,088,405,182,564,846,615,164,214,637,734,671,747,042,841,816,392,654,568,855,171,198,670,204,310,647,596,770,041,328,176,272,480,084,582,031,716,294,514,384,653,096,571,740,071,416,236,945,808,538,413,410,300,289,298,927,252,395,528,565,432,762,746,902,299,537,728,883,871,891,888,920,180,175,626,831,912,680,526,523,939,831,682,287,344,731,935,250,988,843,132,573,781,950,303,895,403,679,727,132,795,397,488,289,174,164,965,209,702,135,337,312,021,434,334,312,951,732,630,812,726,685,025,763,392,191,097,985,766,249,699,687,119,504,279,835,501,519,449,709,324,409,415,408,073,124,865,462,545,188,189,851,584,962,820,403,391,548,396,601,373,059,528,432,677,446,640,585,197,923,376,516,971,120,013,543,334,131,938,417,888,061,074,117,612,732,303,756,778,467,970,163,777,923,331,639,234,305,215,709,299,572,337,761,757,035,510,153,856,294,832,610,278,918,707,689,049,035,382,129,356,527,046,941,595,295,240,080,735,531,452,932,093,120,954,708,385,449,412,443,070,924,916,585,818,586,680,574,886,184,182,869,327,998,509,170,260,455,635,446,673,449,213,667,468,574,497,166,352,796,712,515,091,327,645,628,750,241,110,507,759,853,113,479,841,485,935,309,248,994,530,027,404,986,592,273,392,579,816,513,233,358,532,974,083,317,237,346,360,753,309,422,925,190,491,271,424,500,891,419,608,328,395,756,832,810,762,266,683,352,128,675,634,710,527,614,321,150,264,678,943,631,196,036,179,171,716,403,147,146,831,648,828,350,037,974,238,822,550,903,062,247,261,364,597,892,072,925,954,092,413,870,637,316,678,081,963,842,074,813,957,351,386,937,129,800,909,906,306,557,789,158,351,467,739,260,857,109,711,439,017,680,124,989,017,079,743,044,145,771,243,475,158,059,147,388,018,714,931,793,406,684,348,047,066,043,445,686,177,403,209,935,784,881,349,886,420,722,418,431,870,954,052,668,401,631,196,236,306,698,392,457,910,980,406,842,887,671,834,754,179,287,753,604,831,182,219,354,347,006,159,916,298,311,506,700,806,419,133,922,129,045,611,604,789,404,880,962,986,447,269,926,587,853,558,493,111,142,546,189,040,791,544,888,879,533,624,391,117,247,722,961,529,184,560,333,182,143,385,935,164,557,921,429,635,788,460,812,145,451,792,228,269,895,641,717,313,447,855,596,174,724,491,244,793,968,842,948,355,957,576,317,391,057,576,885,263,334,995,728,821,917,585,989,247,726,539,952,718,769,475,840,182,868,192,342,315,042,817,482,680,980,989,496,896,501,755,261,761,502,450,606,171,506,098,974,822,921,493,874,400,207,047,960,353,579,365,573,551,102,433,938,746,563,463,036,728,374,276,870,017,575,273,546,411,534,018,816,856,133,958,796,375,525,337,544,207,431,860,691,381,011,828,338,496,128,914,496,884,503,314,383,707,471,558,515,557,061,617,255,675,934,959,405,747,919,509,467,910,252,801,546,835,131,737,202,162,139,136 possible values.↩︎

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On Analog Holes and Streaming Schemes

2024-09-19T00:00:00Z

Discussions of digital rights management and copy protection sometimes talk about the analog hole—the lamentable fact (from the perspective of “content rights holders”) that there is always a moment where digital content escapes copy protection.

As digital data, culture can be encrypted and subject to license checks and other forms of rights protection. Once books, music, images, and movies become bits, they can be cryptographically verified and managed. Video games can check that you have access (i.e. are fully paid up) before letting you play; an eBook can check to see if it has “expired” before it displays its text. But no person reads, views, or listens to digital data directly. Some mediating software or hardware always transforms that data into legible text, audible sound, or viewable video. And in that moment of transformation, all those carefully managed ones and zeroes become perceptible, but unruly, sound or light. As those bits briefly, frailly escape the digital, they can be captured, copied, or otherwise recorded. Such copying might be crude: taping a DRM-protected music file onto a cassette as it plays; paging through the eBook and taking pictures of (or transcribing!) the text; aiming your phone at a screen to record the copy-protected video as it streams on your television. When the data is sensible, it is capturable.

That is the analog hole—the opening in the circuit of digital circulation when data escapes its binary representation and leaks into some analog format. In the circuit of cultural consumption, the necessity of the analog hole reflects the embarassing fact that people cannot perceive the bits that make up a digital text. In some basic way, we cannot experience digital media without it being transformed into analog form by dull technologies we usually ignore—the technologies which push air and light around rather than (hushed silence and feeling of awe) data; the technologies we call peripheral. And yet this gap—this hole—in the system is also its sole justification; absent that moment of transformation, the data would be of no use, and the circuit would lose its motivation entirely.

I was reminded of this circuit, in which human perception is figured as a lamentable embarassment (a hole to be closed—or at least narrowed as much as possible), when I read the story of Michael Smith, a North Carolina man who earned more than a million dollars with “fake” music streams (Millman) [source]. (Interestingly, Spotify claims that despite its size as a streaming music platform, only 0.6% of the money Smith earned; i.e., he was exploiting the other streaming services.)

The story has an “AI” angle, but I think its interest is in fact much more basic, because there is a a sort of “hole” here—that plays a structurally similar role even if it is not exactly analog. Smith’s scheme worked by first creating songs, that he owned (and so could collect royalties on). He then set up a network of accounts to automatically and continuously play those songs so that he could collect the royalties. At sufficient scale, it turns out, this can be profitable.

In order to work, these songs (here’s the hole) must be “played” in real time, even if only in a virtualized web browser. The song may never actually be materialized as analog sound (it almost certainly wasn’t!), but each audio frame must be dutifully unpacked, decoded and processed, counted like beads on a faithless rosary. Only then is type incarnated as token, the spatialized block of data representing “a song” become the temporal fact of “a stream,” countable for purposes of monetization.I have to assume there is some minimum song length for a stream to count; or that streams are scaled to song length in someway, to avoid incentivizing 1-second long songs; this seems to suggest 30 seconds might be the minimum.

Smith used 1,040 accounts, each of which could stream “approximately 636 songs per day” (which sounds like an average song length of ~2.25 minutes), totaling more than a half-million streams a day.The indictment says that Smith “had 52 cloud services accounts, and each of those accounts had 20 Bot Accounts on the Streaming Platforms.” I suppose that means that he had set 52 instances (of some kind; AWS, virtualized servers…), running, in effect, 20 browser tabs playing his songs day and night. Of course, if all those bots streamed a single song, his scheme would be detected, so Smith needed many songs (thousands of songs) that he could stream, and so collect a small royalty on each (shades of salami slicing). Smith ultimately began using AI to generate those songs; but AI seems incidental to the story. For one thing, many of Smith’s songs were uploaded in 2019 or earlier—too early for the current crop of AI-song generation. When Smith started using AI to generate songs, he remarked how different the new technology is. “Song quality is 10x-20x better now,” he marvels in an email (qtd. in United States District Court, Southern District of New York, sec.24). More essentially, the scheme is enabled less by AI songs, than by the fact that in the monetization process a “song” is merely a sort of empty space in a circuit of cultural consumption.

Like the “analog hole,” what Smith’s scheme exploits is the moment that escapes the otherwise closed circuit of data circulation. In a streaming scam, the music itself is utterly irrelevant but (for the would-be profiteer) frustratingly essential. Like the content protected by DRM, it is both essential (without content, no need for DRM; without music, nothing to stream) and irrelevant to the monetization scheme (so long as you’re streaming, we don’t care what!). Taste itself is certainly not outside algorithmic analysis and manipulation Fry, but the ownership of digital data divorces the fact of ownership from the data itself, and the monetization of circulation (through both streaming and DRM) creates an antipathy between the object and its consumption. Music itself is incidental to the entire process. In an email, Smith wrote “Keep in mind what we’re doing musically here… this is not ‘music,’ it’s ‘instant music’” (United States District Court, Southern District of New York, sec.20). Simply posting silence or random noise would have been risky; Spotify, at least, was already cracking down on “functional noises” as a vector for fraud. But “instant music” isn’t quite right. Smith’s tracks are “music-ish”; they aren’t music, but they are shaped enough like it to bridge the small gap in the monetization circuit that lets the money flow.

Works Cited

Fry, Hannah. Hello World: Being Human in the Age of Algorithms. W. W. Norton & Company, 2019.

Millman, Ethan. Feds Indict Musician on Landmark Massive Streaming Fraud Charges. Rolling Stone, 4 Sept. 2024, https://www.rollingstone.com/music/music-news/feds-arrest-musician-on-massive-streaming-fraud-claims-1235095009/.

Seaver, Nick. Computing Taste: Algorithms and the Makers of Music Recommendation. the University of Chicago Press, 2022.

United States District Court, Southern District of New York. U.S. V. Smith Indictment. 4 Sept. 2024, https://www.justice.gov/usao-sdny/media/1366241.

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The Case of Cluster 4: A Data Mystery

2024-06-24T00:00:00Z

As I’ve previously discussed, I’m working with a dataset combining page-level word counts I am just now beginning to explore using HathiTrust Data Capsules to access full-text. I’m quite late to this party, but they seem kinda awesome. from HathiTrust with the At the Circulating Library bibliography to investigate the three-volume novel as something like a publishing platform. In the course of that investigation I encountered a minor data mystery; this post summarizes it. Spoiler: after 1880 three-volume novels included more advertising—or, more precisely, particular publishers did (Chatto and Windus especially). These pages of advertisement contribute noise to analysis of these texts and should probably be removed for most types of analysis.

Clustering the Three-Volume Novel

It took me a while to convince myself of this conclusion, however, and this post records that process of self-persuasion. Let’s start with this image: You can get larger versions of the plots on this page by right-clicking and opening the image in a new tab.

Jittered points in 5 clusters detected; each point represents a single novel.

This graph shows all the three-volume novels listed in the ATCL bibliography that I have (so far) been able to identify in HathiTrust. Each dot represents a single novel. The novels have been represented as bags of words, generated from HathiTrust’s extracted features dataset, and then clustered using k-means clustering. I wondered if this sort of unsupervised clustering could identify genres within the dataset. While the three-volume format imposed certain kinds of uniformity across novels (especially in terms of length), a variety of genres thrived within this constraint. One critic, for instance, describes three central strands in “Victorian Popular Fiction”: “In minor fiction the major interests lie in the sensational novel, the romance or domestic love story, and the adventure tale, either historical or geographically exotic” (Terry 12). Would a clustering algorithm locate those (or other) trends?

After trying a few k values (and using the somewhat dubious “elbow method” to find a number for k, I settled on five clusters. (I’m continuing to explore ways to cluster this data, but this post is about something odd I noticed along the way.) And it is the result of that clustering that is visualized above. Each point represents a novel; its location on the x-axis places it within one of five clusters; its vertical position shows its year of publication (the points are “jittered” and slightly transparent to aid viewing). To me, such a visualization is a useful way of getting an overview of this data; it shows not just how many novels were identified in each cluster, but offers some sense of trends over time.

Perhaps you can detect some trends. The number of novels published per year increases across the dataset, so the increased number of points towards the top makes sense. Clusters 0 and 1 seem to increase, cluster 2 may decrease and cluster 3 seems mostly consistent. Maybe. But surely what jumps out is cluster 4. What’s going on with cluster 4?

We have no insight yet into what similarities (what sets of word frequencies) are producing these clusters. Looking only at this visualization it is impossible to know if these clusters are meaningful at all. We could look over the titles of the novels in each cluster to see if the clusters make sense—but while interesting to look at (WARNING: large filesize—a 3 meg image), the titles alone are hard to parse.I will note that works by a single author do seem to collect within a cluster, which is promising I think.

To investigate further, I used tf-idf to identify the terms most distinctive to each cluster. (I added all the novels from each cluster together, in effect comparing five long novels to one another). Here are the top-scoring (per tf-idf scores, as implemented by scikit-learn terms for cluster 4:

4: ['country', 'boy', 'understand', 'certain', 'really', 'master', 'ye', 'fellow', 'open', 'lay', 'dark', 'death', 'women', 'large', 'case', 'passed', 'tlie', 'nature', 'doubt', 'ask', 'hard', 'family', 'doctor', 'black', 'replied', 'read', 'son', 'god', 'air', 'brother', 'sat', 'wish', 'second', 'child', 'talk', 'water', 'suppose', 'called', 'help', 'brought', 'till', 'strange', 'present', 'dead', 'friends', 'captain', 'having', 've', 'hear', 'leave', 'began', 'rev', 'true', 'gave', 'story', 'mean', '7s', 'taken', 'believe', 'sea', 'cried', 'edited', 'set', 'matter', 'speak', 'hope', 'white', 'history', 'ready', 'word', 'gone', 'soon', 'light', 'end', 'does', 'sure', 'days', 'book', 'kind', 'boards', 'money', 'best', 'english', 'morning', 'london', 'looking', 'john', '3s', 'stood', 'want', 'answered', 'seen', 'small', 'wife', 'words', 'turned', 'friend', 'works', 'lord', 'far', 'years', 'round', 'door', 'hands', 'felt', '6s', 'right', 'voice', 'heard', 'got', 'home', 'course', 'work', 'told', 'better', 'took', 'thing', 'half', 'cr', 'saw', 'vols', 'yes', 'going', 'things', 'night', 'mother', 'll', 'asked', 'poor', 'moment', 'dear', 'left', 'girl', 'vol', 'mind', 'world', 'place', 'quite', 'heart', 'knew', 'head', 'oh', 'miss', 'let', 'people', 'illustrations', 'room', 'woman', '2s', 'father', 'post', 'illustrated', 'sir', 'love', 'look', 'just', 'house', 'lady', 'tell', 'looked', 'edition', 'went', 'men', 'make', 'shall', 'hand', 'young', 'new', 'thought', 'away', 'extra', 'long', 'came', 'great', 'eyes', 'face', 'day', 'mrs', 'way', 'say', 'think', 'come', 'life', 'old', 'good', 'svo', 'know', 'time', 'cloth', 'crown', 'like', 'little', 'did', 'mr', '6d', '8vo', 'lrb', 'man', 'rrb', 'said']

Such a list of terms invites critical fabulation: perhaps this cluster of novels, appearing largely after 1880, is from novels about country boys who come to really understand certain elements of nature, such as dark death; and perhaps these novels became especially compelling starting in the wake of the passage of the Reform Act of 1867 and the Education Act of 1870, as a certain vision of rural working-class simplicity was increasingly complicated, especially in the minds of the educated middle class who formed a crucial part of the readership of three-volume novels. Is that a compelling story? (I hope not.)

What jumped out to me in this list were tokens like 7s, 3s, 6s, half, cr, vols, vol, illustrations, 2s, illustrated, 6d, and 8vo. Some (7s, 3s, 6s, 2s, 6d) are British, pre-decimalization prices. Others (half, cr [short for crown], vols, vol, illustrations, and 8vo) are terms for book format. And both prices and book format terms are typical of book advertisement lists.

I am now pretty confident that initial hunch was right: cluster 4 was created by changes in advertising practice. The remainder of this post is about how I convinced myself that this hunch was true.

A Laborious Exercise in Hunch-Proving

I first searched through the texts to find pages where those terms for money and format were most common. These pages, indeed, were typically advertising, almost always at the end of volumes. From this process of manual inspection, I identified 200 pages of advertising in the dataset; I also collected 200 pages from novels in the dataset that I assumed were not advertising (by randomly selecting pages from the middle 75% of volumes). These 400 pages offered a dataset from which I trained a logistic regression classifier to distinguish advertising from non-advertising pages. Holding out a quarter of pages as a test set, the classifier reported 100% accuracy on the held-out test data.

I then used that classifier on every page in the dataset (of three-volume novels listed in ATCL and identified in HathiTrust), identifying about 6,100 pages of advertising in the entire dataset. With this data I was able to begin to investigate the presence of advertising across my sample of three-volume novels.

An important first summary is offered by this graph:

Comparison of the number of titles listed in ATCL, number of titles identified in HathiTrust, and number of titles with advertising.

First: this graph reminds me that I am looking at a subset of a much larger bibliography. The ATCL bibliography (when I last downloaded it; it is continuously updated and I’m due for a re-up) listed 22,000 titles. Of those about 5,000 are three-volume novels. And within those 5,000 three-volume novels in ATCL I have identified about 1,000 in HathiTrust—or nearly 20%. But that 20% is not consistent across publishers.

Publisher	Titles in ATCL	Titles identified in HathiTrust	Percentage Identified
Hurst and Blackett	878	152	17.3
Bentley	755	173	22.9
Tinsley Brothers	486	84	17.3
T. C. Newby	375	27	7.2
Chapman and Hall	295	73	24.7
Henry Colburn	269	25	9.3
F. V. White	202	19	9.4
Chatto and Windus	197	48	24.4
Smith, Elder	190	49	25.8
Sampson Low	187	37	19.8
Saunders and Otley	153	18	11.8
Samuel Tinsley	151	12	7.9
Ward and Downey	122	25	20.5
John Maxwell	91	3	3.3
Macmillan	89	34	38.2
Blackwood	86	36	41.9
Longman	68	29	42.6
Remington	67	9	13.4
C. J. Skeet	62	3	4.8
Hutchinson	38	11	28.9

For some reason, for instance, I’ve only identified 3 of the 91 titles published by John Maxwell. This may be a failure in how I’m finding novels. It may be attributable to the HathiTrust dataset itself. The ATCL bibliography is focused on British publishing Indeed, the three-volume novel is a British phenomenon; the artificial price-structure that enabled it did not operate in the U.S. or continental Europe. but HathiTrust’s collection is a culled from U.S. libraries; this may explain the gaps. I really don’t know and I don’t think any of this issues affect the particular mystery I’m trying to solve (the case of cluster 4), but it is worth remembering.

These provisos acknowledged, I think we can say that publishers varied widely in whether they included advertising. Hurst and Blackett, the biggest publisher in the dataset, included advertising in most of their titles; but many publishers included none at all.

Within my data, only three publishers included advertisements in more than half of their three-volume titles: Chatto and Windus, Hurst and Blackett, and Macmillan.

But this observation is further complicated by additional variables. First: publishers change their practices over time and so these percentages may be hiding trends within the output of any publisher. Second, as a measure of advertising the “percentage of titles with ads” conflates a single page of advertising with multiple pages. And, indeed, some titles have a single page of advertisements, while others may include many pages of advertisements. This latter point includes a further complication: regardless of the number of pages, a publisher may adjust the size of the typeface to fit more text on a single page. In short, “percentage of titles with advertisements” obscures two variables: time and amount of advertising (where advertising is probably measured in words of advertisement).

Let’s first look at select advertisers to investigate trends in advertisement (here measured in pages):

Trends in Advertising Among Select Publishers

The red shaded area represents the the years when cluster 4 emerges (1880–1897). While these trends are a bit jagged (there’s no smoothing here), it seems clear that in the period of interest Chatto and Windus emerges as the main contributor of advertising to this dataset.

We can also look at how many words of advertising per page were detected. I’ve added smoothing lines to make the trends clearer: while Hurst and Blackett had on average more words per page of advertising than other publishers, they had fewer words per page of advertising than either Macmillan or Chatto and Windus, publishers who increased their total pages of advertising in the period when cluster 4 emerges.

It is true that “advertising increases” during the period when cluster 4 emerges. This is true both in terms of an increase in the total number of pages of advertising over the period and in the amount of advertising per page. The increase in words per page is especially worth noting because once those words were added to the rest of the text, for purposes of clustering, they would have had an effect larger than even the number of pages would suggest.

Even as advertising is increasing across this period, this increase, however, is not uniform; it reflects particular publishers, especially Chatto and Windus.

So far we have a cluster that emerges in a period; a series of trends in advertising that increase in that period; and some words in the tf-idf scores that suggest a connection. But it was the following two visualizations that convinced me that cluster 4 was produced by these changes in advertising.

The first is a version of my original visualization, now color coded to contrast books published by Chatto and Windus with other publishers.

While Cluster 4 is not generated exclusively by Chatto and Windus, Chatto and Windus do dominate that cluster. 48% percent of the works in cluster 4 were published by Chatto and Windus (who was responsible for 10% of the books published in the period from 1880 to 1897); and 62% of the 42 works published by Chatto and Windus in the period were classified as cluster 4. So—there is strong correlation between cluster four and Chatto and Windus.

A second image color codes the points based on the number of pages of advertising detected in the title.

These visualizations convince me that cluster 4 is a product of a change in how much advertising was included in the three-volume novels in the dataset, with the bulk of the change produced by Chatto and Windus.

And, having thus laboriously concluded that advertising was contaminating the clustering, it is a relatively simple matter to remove the advertising pages and cluster again. Doing so eliminates the anomalous cluster that emerges around 1880.

There would be other things to say about this new clustering. K-means clustering is a stochastic process, so one can’t compare separate clusterings directly. It is especially important to not see any of these clusters as directly corresponding to the old cluster 4. This is simply a new way of splitting up the ~1000 novels in this dataset. However, by removing the pages I identified as advertising we also eliminate the appearance of a cluster that emerges around 1880.

Advertisement in the Time of the Triple-Decker’s Decline

You may be wondering: what did these pages of advertisements look like? There is some range, but they are mostly book lists published by the publisher featuring “New Books” (or New Books this Month). They typically list author, title, and some short description of the book object itself (e.g. cloth, illustrated; sometimes a price). They could be quite simple, as this list of “Cheap Editions of Popular Novels.”

Source: List of “Cheap Editions of Popular Novels” from the back pages of Queen Cophetua by R. E. Francillon (1880)

They could also be more elaborate, like this page—the first of a more than 30-page addendum listing Chatto and Windus titles.

Source: A Chatto Windus Book List (appearing at the back of a volume of Blind Love by Wilkie Collins, published by Chatto and Windus in 1889).]

At times an entire page would be devoted to a single work, as in this advertisement for the second volume of what I take to be a history of the Tower of London. It includes extensive quotations from reviews as well as a complete table of contents. The book list then continues for another sixteen pages of advertisements (most significantly shorter) after than this one.

Source: Hurst and Blackett Book List appearing at back of a volum of Annals of an Eventful Life by George Webbe Dasent (1870)

These lists advertize works in a variety of genres; they sometimes have their own pagination and were presumably added to many different novels as they were being published.

They are mostly advertisements of books for sale—rather than for lending from a private library. While in one list Chatto and Windus offers a short list of “New Novels at All Libraries,” most of these lists directly address consumers—offering works for sale (“Sold by booksellers, or sent post-free for the published price by the Publishers”) to readers at prices more reasonable (often in the 2–6 shillings range) than the three-volume novel’s inflated retail price of one and half guineas. It is interesting that these lists increase in the 1880s, as this is the period when the three-volume novel (and the market structure of lending libraries) were beginning to decline. These lists might be seen (either as cause or effect) of the decline of the three-volume novel’s dominance, which was inextricable from the subscription library model.

Concluding Methodological Reflection

This mystery emerged from a fluke in the data. But one’s man’s noise (“Can I cluster the genres of the three-volume novel?”) is another man’s signal (“Increasing advertisement lists index the waning of the business model of the three-volume model.”). I don’t know that this is a new insight about the period, the three-volume novel, or Victorian publishing, but the increase in advertising for its alternatives (cheaper, single volume novels) is certainly part of the picture of the three-volume novel’s decline.

It also ends up being a potentially valuable dataset of its own. Advertising lists contain information about price and genre; they highlight prominent reviewing journals. They could also point to titles not yet identified in ATCL or other bibliographies. The tendency one sees in even simple book lists, for instance, to identify novels by their authors seems an important and telling detail of how novels were marketed.

And, of course, identifying these pages allows them to be removed, which will in turn (I hope) improve results when I return to clustering these novels.

But if I am trying to talk up the benefits of this exploration of advertising lists in three-volume novels, it is mostly because of how laborious it has been. It is a cliché that 80% (at least!) of data analysis is data cleaning. But cleaning, as a metaphor, may obscure how demanding this work can be. The cleaning metaphor obscures, as in this case, that figuring out what was dirt and what was data was the challenge. Complex, historical datasets (like HathiTrust data) present these sort of unique challenges, where investigating even a banal question (did advertising increase in three-volume novels?) involves an enormous amount of care to avoid making mistakes.Which is not to discount the possibility that I have missed something!

Works Cited

Terry, R. C. Victorian Popular Fiction, 1860-80. Humanities Press, 1983.

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Zadie Smith’s The Fraud

2023-10-16T00:00:00Z

So, I recently finished Zadie Smith’s latest novel, The Fraud. I am an ardent Smith fan, and… I liked this book a lot! This is how literature professors talk about literature, right?

I was waiting until I’d finished The Fraud, to read Andrea Long Chu’s review, which I had seen making the rounds a few weeks ago. Chu faults Smith’s “humanism” for the novel’s “realism.” Rather than a properly political perspective, Chu suggests, the novel proffers only the realist novelist’s milquetoast: empathy. Chu finds something (though exactly what isn’t clear; perhaps “its audacious unreality”) more exciting and biting in Smith’s first novel, White Teeth. But to call the novel “realist” oversimplifies things—probably as much as simply calling Smith a “humanist.”

While I like White Teeth, I have taught White Teeth—using it, in the ruthless metonymic calculus of the syllabus (wherein every work must also show us some more general trend), as an instance of postmodernism. And it works well! I found myself comparing it in instead to my preferred Smith novel: NW. It shares with that novel a juggled narrative chronology as well as short chapters, with commentary-like titles (moments where authorial wit often peeks through). It shares with NW a preoccupation with certain neighborhoods in northwest London. The two novels share a number of touchstones: John Ball’s sermon (“When Adam delved and Eve Span”), Our Lady of Willesden Church, and even a key secondary character named Bogle(!). And they are both are fundamentally novels of social encounter. The Fraud’s central character asks, what can we know of others? And that question is riven by class and race. This is the Forsterian theme of Howards End that Smith has pursued in On Beauty and elsewhere.

The Fraud bears marks of the last decade of politics. The account of the debates and class politics of the Tichborne case (which are one of the novel’s greatest accomplishments) recall our own Trumpist/Brexit era. References to antivaccination conspiracies only further cement the parallel.

While most discussions of the novel center the Tichborne case; Josephine Tey’s Brat Farrar, while fictionalized, is a novel more centrally concerned with the Tichborne’s identity plot., the Tichborne case is merely the occasion for the novel, rather than its central concern. It is irrelevant to large stretches of the novel, but it provides the key thematic strand around which the rest of the novel is woven. The novel’s most interesting move is to try to unite a novel of nineteenth-century England with the wider context of slavery and the economic base which provided the Empire with its wealth.

The Fraud has absorbed Edward Said’s reading of Mansfield Park, and in its most formally daring move, it tries to reintegrate the geographically disparate elements of the British 19th century into a single narrative whole. This is accomplished chiefly through its treatment of Andrew Bogle in the novel’s second half (in volumes 6 and 7). It is a provocative move, and one that invites tempts me to teach it alongside Rhys’s Wide Sargasso Sea. I found myself wishing this move had been a little more formally daring—that these chapters had more completely abandoned the novel’s earlier narrative mode, or that these chapters had more completely dislocated Eliza. They instead offer something like a hiatus rather than completely (to use a metaphor the novel repeats) turning the novel’s world upside down.

Like NW, this is a novel that is fascinated by sexuality but which never fully engages it. In NW a sort of sexualized racial masquerade emerges late in the novel, as Keisha’s character enters a moment of crisis. Here Eliza’s relationships with both Ainsworth’s first wife and with Ainsworth himself (to speak nothing of the inchoate but unrealizable cross-race romance between Eliza and Andrew Bogle), gesture towards desire. Such desire complicates the social relationships of the novel. But it also complicates the empathy that Chu fairly identifies as central concerns of this novel and of Smith’s work more broadly. Yet sexuality, in neither The Fraud or NW, ever takes center stage. This may simply confirm the degree to which sexuality has always been a troubling force for any essentially humanist view of the world. That Smith engages sex in this glancing way may suggest a failure to tackle fully the queerness that the novel courts. But it also suggests that Smith is her own best critic of humanism’s naivetes. That doesn’t mean, however, that the novel offers a fully coherent account of sexuality, anymore than it offers a totally satisfying politics. (Whether one would want such a thing from a novel may be fundamental point of disagreement.)

Finally, a note on character. Chu suggests that, “The irony of Smith’s career is that she has never actually excelled at constructing the kind of sympathetic, all-too-human characters she advocates for.” Another critic reports, “her characters this time around—Eliza, Ainsworth, Sarah, and the rest—feel more like archetypes than like people.” Now, “how real is this character?” seems like a losing game no matter how it is played; but I’ll play for a moment: I simply don’t agree with these assessments. I don’t know what grounds there could be for justification of such claims beyond the lousy proffering of mere examples, so, I’ll try that. Sarah Ainsworth (the second Ainsworth wife) is a rich and sympathetic “Trumpist” character. (The sort of character in whom one may catch glimpses of people you’ve known.) And the account, near the novel’s closing, of Eliza discovering William Ainsworth’s dead body I found utterly compelling. It has stuck with me and feels like a consummation; it repeats the representation of Ainsworth’s fundamental superficiality, but more crucially of Eliza’s self-alienation—her misrecognition of her feelings even of someone so fundamentally superficial. It is a compelling moment. Fine, I’ll say it, it is a touching moment! The empathy got through! This is a novel that does not blithely offer empathy as a salve for fascist politics. Smith shows Eliza’s capacity for empathy, but more crucially (in another late scene—a very Victorian scene of inheritance), she shows the ways that empathy fails, and the ways that empathy escapes and complicates any coherent sense of selfhood.

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How Long was the Three Volume Novel?

2023-08-23T00:00:00Z

I’ve been working with data related to the three-volume novel recently. I’ll be writing a few posts as I try to tie this project together. In this post I’d like to talk just about the length of three-volume novels, and a bit of a challenge I encountered.

The Three-Volume Novel

The three-volume novel (or “triple-decker”) is a distinctly Victorian format for the publication of fiction. While multivolume novels are as old as the form itself (Tristram Shandy [1759-1767] was initially in nine volumes; Richardson’s Clarissa [1748] was published in seven volumes), the three-volume format came to be dominant in the Victorian period.

Philip Gaskell summarizes the three-volume novel like this:

The mid-nineteenth-century English three-decker was typically a cloth-cased post octavo with about 20 gatherings, 320 pages, per volume. The edges were often uncut, and the type was small pica, or less commonly pica, with up to a nonpareil of lead between the lines (i.e. 10 or 12 pt., up to 6pt. leaded). There were 900-920 pages of text, containing 150,000-200,000 words, padded out with heavy leading, wide margins, and extravagant chapter divisions. The median length of a large sample of three-deckers was actually 168,000 words, divided into 45 chapters… Not all mid-nineteenth-century fiction was first published in three-decker form. About a third of the novels that were first published in book form came out in one or two (or very occasionally four) volumes. (301)¹

The predominance of the three-volume format was a function of private lending libraries. Novels in three volumes were quite expensive (one and a half guineas, or one pound eleven shillings and six pence).The “guinea” is an odd measure of value, equal to a pound a shilling. It was, per the OED, “the ordinary unit for a professional fee and for a subscription to a society or institution” (“Guinea”). That is—expressing the price in guineas has class connotations, which are not irrelevant here. Because they were so expensive, readers did not purchase triple-deckers—they borrowed them, one volume at a time typically, from private lending libraries like Mudie’s. The three-volume novel became the standard format in which such libraries purchased novels.². The three-volume novel, however, had disappeared by the turn of the twentieth century.

The three-volume novel therefore represents a rigid aesthetic and financial structure. It integrates a method of circulation and “monetization” (the subscription library) with an aesthetic/narrative mode. To crudely adapt some Marxist vocabulary, the three-volume novel is an interesting test case for broad questions about the relation of narrative superstructure to its enabling market base. Just as today we might ask “why does everything on Netflix look like that?” (Jackson), we might wonder if it is possible to detect a homogeneizing or otherwise shaping effect of the circulating library on the narratives and aesthetics of the British novel in the Victorian.

I am exploring such questions by taking advantage of Troy Bassett’s invaluable bibliography At the Circulating Library for a list of titles, and culling data from the volumes available via HathiTrust . I have used the aggregated HathiFiles digest of HathiTrust holdings to find titles listed in At the Circulating Library. This has been the most technically complicated part of this process. That, however, is a separate issue that I will skip over now. Let me simply say that I was able to locate 956 complete three-volume novels (represented by 2868 individual volumes), as well as 105 single volume novels from the ATCL bibliography, in HathiTrust.

We can use these titles to begin to investigate some of these questions. In this post I’ll look at a single, apparently simple, and ruthlessly quantitative, measure of the three-volume. How long were they?

Lengths

The ATCL bibliography contains more than 5,000 three-volume novels. I have located 956 of those titles in HathiTrust and counted their lengths using the following basic code. Using the Python htrc_features library, one can count the words in a volue with this code.

tokens = vol.tokens_per_page(section='body').sum()

Once I had found about a thousand novels and computed their lengths I started graphing ’em. (I’ll share those graphs in a moment.) But I wanted some check to see if my counts seemd right. Thankfully, mine is not the first attempt to quantify the length of the three-volume novel. In 1957, Charles and Edward Lauterbach published an analysis of the three-volume novel in The Papers of the Bibliographical Society of America. They attempt to quantify the three-volume novel in terms of length (in words and pages) as well as more fine-grained measures such as the size of the page, size of the type area, typeface size, and so on. Their data, summarizing these elements for 105 novels, is impressive. How, you may wonder, did they manage this in a predigital age? They explain,

Making the word count of a hundred five nineteenth-century would be impossible without mechanical aids and short-cuts. An electrical counter was developed which made it possible to count about 10,000 words an hour with only slight error. (263)

An electrical counter? Exactly what this means is not clear. In a footnote they promise “A paper describing this eletrical bibliographic aid,” but I have found no record of this paper beyond this footnote—and so, for now, I remain uncertain exactly how the Lauterbachs counted all these words.

However, the Lauterbach data provide a useful point of comparison. I used a trial of Amazon’s OCR service to convert the tables from Lauterbach’s publication, cleaned them up a bit (CSV). The mean length of a novel in Lauterbach’s collection is 171,038 words (median, 170,493 words).You can get larger versions of the plots on this page by right-clicking and opening the image in a new tab.

Histogram of Lauterbach Book Length Data

The data I derived from HathiTrust looks like this, with a mean of 194,818 words (median 190,427 words).

Histogram of Book Length Data Derived from HathiTrust and ATCL

Because of the very different number of titles (105 in Lauterbauch, and almost 1000 in the HathiTrust-derived dataset), a boxplot makes a comparison clearer.

Box Plot Comparing Lauterbach Data with HathiTrust-Derived Data

I had expected (and indeed hoped) that the Lauterbach data would simply confirm that my method of tallying three-volume novels from HathiTrust data was accurate. So when I saw this box plot (and for quite a while afterwards) my heart sunk. There is a significant difference between the means of these two datasets (Lauterbach mean=171038, HathiTrust mean=194818)Welch’s t-test returns a p-value of 0.000003797; which suggests the difference between these two populations is not mere chance. which should ostensibly be describing the same general phenomenon.

When I first tried to explain this discrepancy, I noted that Lauterbach and Lauterbach’s sense of the three-volume novel includes a number of pre-Victorian texts; it includes, for instance, Frankenstein (published in three-volumes in 1818) as well as fifteen other texts published before 1827 (the earliest date of the ATCL-derived, HathiTrust data). The Lauterbachs’ data include novels that just happen to have been published in three-volumes, not necessarily “three-volume” novels of the sort produced by the rise of the lending libraries. So I removed them from the dataset, assuming that pre-Victorian three-volumes were shorter (Frankenstein is the shortest novel in the dataset!), and that these novels were reducing the Lauterbach’s data.

But removing those texts made no real difference (the reduced set is “Lauterbach Adjusted” in the following boxplot, mean length 173,284 words).

Box Plot Comparing Lauterbach Data with Original HathiTrust-Derived Data

Perhaps, dear reader, you noted the blithe way above I glossed over a rather important detail in how I counted the length of the HathiTrust texts in the code above. If you noticed it, congratulations! Sadly, I did not (without a lot of frustration). My counts were sums of tokens. That includes tokens representing punctuation.

I revised my code to not count tokens that had been tagged with the following part of speech tags: '$', '-RRB-', '-LRB-', ',', '.', "''", ':', '``'³ With that data, I got the following boxplot.

Box Plot Comparing Lauterbach Data with Adjusted HathiTrust-Derived Data

Here the median of the HT data is now 159,814 words.A Welch’s T-Test between the HT and Lauterbach data results in a p-value of 0.08, which suggests the difference may not be significant, for whatever that’s worth.

While there is still a detectable difference, it is smaller, and seems consistent with what we see if we compare the number of pages reported by Lauterbach and in the ATCL titles detected in HathiTrust:

Box Plot Comparing Number of Pages in Three-Volume Novels, Lauterbach Data with HathiTrust-Derived Data

I suspect that it would make sense to further fine-tune which tokens are counted, but this adjustment satisfied me that my data were telling the same basic story as Lauterbach’s (and so would provide a safe basis for additional, more sophisticated analyses).

Length Over Time

Based on this comparison with Lauterbach’s data, we can be somewhat confident in other uses of this data extracted from HT. The more interesting uses will include inferences about the narratives themselves. Before I end this post, however, it is worth looking at the length of the three volume over time. So far, my data has only confirmed the story told Lauterbach, Gaskell, and Bassett. Combining more titles (provided by Bassett’s bibliography) with an analysis of length (akin to Lauterbach’s), we can look at something none of those critics have considered—what happened to the length of the three-volume novel over its history.

Scatter Plot Showing Length of Three-Volume Novels (in words) Over time

Three-volume novels get shorter over the 70-year period represented by this data. The trend is slight, and easier to see in these histograms, split into three periods.

Histogram of Novel Lengths (in words) Divided into Three periods

Histogram of Novel Lengths (in pages) Divided into Three periods

Over its history, the three-volume novel got shorter by almost 30,000 words, or about 90 pages. That seems at least a little interesting! More interesting things, though, can done with this data—in a future post.

Works Cited

Bassett, Troy J. At the Circulating Library. At the Circulating Library: A Database of Victorian Fiction, 1837–1901, https://www.victorianresearch.org/atcl/.

Gaskell, Philip. A New Introduction to Bibliography. Reprinted with corrections in 1995, Oak Knoll Press, 2007.

“Guinea.” OED, https://www.oed.com/dictionary/guinea_n?tab=meaning_and_use#2320395. Accessed 16 Aug. 2023.

Jackson, Gita. Why Does Everything On Netflix Look Like That? Vice, 15 Aug. 2022, https://www.vice.com/en/article/ake3j5/why-does-everything-on-netflix-look-like-that.

Lauterbach, Charles E., and Edward S. Lauterbach. “The Nineteenth Century Three-Volume Novel.” The Papers of the Bibliographical Society of America, vol. 51, no. 4, 1957, pp. 263–302, https://www.jstor.org/stable/24299448.

Wilson, Nicola. “Circulating Morals.” Prudes on the Prowl: Fiction and Obscenity in England, 1850 to the Present Day, edited by David Bradshaw and Rachel Potter, 1st ed, Oxford University Press, 2013, pp. 52–70.

I need to check Gaskell—this quotation comes from a note in my Zotero library; I assume he is citing Lauterbach for this data, but not sure.↩︎
Such libraries were crucial instituations of Victorian middle-class reading. If a library opted not to purchase a novel (as famously happened to George Moore), they could drmatically undermine its potential. This power of the the libraries outlived the three-volume novel format. As Nicola Wilson shows, such libraries operated as gatekeepers (and, essentially, censors), well into the first decade of the twentieth century (Wilson)↩︎
I actually don’t know what some of these tags (-RRB- and -LRB-) represent. I haven’t seem this documented anywhere, and don’t see such tags in other descriptions of Penn’s Treebank tags.↩︎

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Text, not Language: Inexpert Provocations on Language Models

2023-03-16T00:00:00Z

Like lots of folks, I’ve been watching the discourse and debate about ChatGPT in particular, and about Large Language Models more broadly, with great interest. With the release of GPT-4 a couple days ago (as I was trying to write this), the already torrential discourse about these objects has only intensified. It feels impossible to stay on top of the news stories, blog posts, and demos related to these technologies. People are discussing these things with an intensity typically reserved for defining digital humanities.

I’ve felt dissatisfied with the existing discourse in ways I’ve had a hard time articulating; this post tries enunciate that dissastisfication in the form of a series of provocations, none of which are sufficiently justified.

For what it’s worth (and as much as a record for myself), as I write this, these are the essays, articles, and posts that I’ve been thinking about most in the last couple weeks:

Writings by Emily Bender and others: Bender and Koller, “Climing Towards NLU: On Meaning, Form, and Understanding” ; Bender et al, “On the Dangers of Stochastic Parrots” ; this profile of Bender
Ted Chiang, “ChatGPT Is a Blurry JPEG of the Web”
Chomsky et al., “The False Promise of ChatGPT”(chomskyOpinion2023?)
Ted Underwood, “Mapping the Latent Spaces of Culture”
Stephen Wolfram, “What is ChatGPT Doing… and Why Does it Work?” (this is the single best introduction to the technology behind ChatGPT, etc).

Provocations

What “intelligence” is here is human, not machine: Emily Bender, among others, notes that the phrase “artificial intelligence” is hype; and while I am not among those folks who are unimpressed by these models, I think it is useful to recognize how human, and indeed, cultural these objects are. If these models are able to produce output that seems to be motivated by knowledge (or intelligence), it is because of the massive amounts of text on which they were trained, text that was itself generated by (human) intelligence. They are representing, and re-representations, of human intelligence rather than being intelligent themselves.

Everyone has their preferred metaphors here; I’ve seen people describe large language models as essentially extractive technologies, drilling into the existing and accumulated textual record to extract a set of tokens, weights, and probabilities (“text mining,” as they say). I’ve been trying on a slightly different metaphor, imagining them as a sort of massive act of statistical casting, pouring a gelatinous network of neuronal goo into the structured chaos of text on the internet, and allowing gradient descent to gradually harden it into a model. My point, though, in either metaphor, is that what we are marvelling at is less the technology of extraction or casting, but the extracted (or cast) material.
This knowledge is social, not individual: Moreover, the knowledge these models represent is distributed and social; it cannot be owned by one entity, nor localized in a single space. Indeed, since these models are trained on internet text, they are trained, at least potentially, on all of us. As Saffron Huang writes, “We are each, in a small way, an author—perhaps more aptly, a ghostwriter—of ChatGPT” (Huang). Of course, exactly who is an author of ChatGPT is impossible to say, without a reckoning with the training data and its sources.

Their essentially social basis is, I would conjecture, a source of their power. As direct models of collective “text use”, and so as indirect models of collective knowledge, the model can represent “more” than any one person knows. Yet, rather that seeing them as Borg-like, they seem equally describable as embodiments collective/historical intelligence, of the sort that we might also call culture or ideology.¹
Chat as an interface is not great: To judge from results, if you’re trying to productize an LLM, chat is the killer app for marketing the large matrices of numbers that at some level are an LLM.A blog post by Peter Levine making an apposite point comes to my attention via Mastodon as I’m writing this. By adopting the conventions of human conversation, chat bots from ELIZA forward have proven to be shockingly compelling.

Yet, the representation that motivates these technologies is not essentially conversational. ChatGPT takes a large-scale representation of “meaning” and then uses reinforcement learning from human feedback (RLHF) to make interacting with it feel conversational (and, I take it, to sand off the rough edges). This is, though, only one particular interface for these models. The model itself, as Benjamin Schmidt notes, “doesn’t really participate in a conversation—it doesn’t even know which participant in the conversation it is!” (Schmidt). As an interface, though, “chat” predisposes a user to treat the model as a sort of superhumanly knowledgable person. It inspires an immediate anxiety—is it smarter than me? Could it do my job? (Does it want me to leave my wife? (Roose)) All the specular logic of the doppleganger—jealous rivalry!—is right there, as a consequence of this interface choice. But these models are far more like a talking library (a weird, peculiarly curated library) than they are a person.
These are not models of language but models of text: We call these “large language models,” but they are not, in fact, models of language. They are models made from language, and which make language. But they have “learned” language the same way that they have “learned” the other things they are able to produce compelling output about. They generate well-formed, grammatical sentences because grammaticality is a property of the training data. That training data, though, is better understood as text rather than language. This accounts for the ease with the models seem to generate non-natural language text (say, computer programs). The tokens that GPT learns, for instance, are not words or any other linguistic objection, but are a product of a tokenization scheme that has proven useful for this task (see the OpenAI tokenizer).

And the text data on which they are trained is not language. This is true in a few senses; its data is exclusively written (rather than being spoken or signed), and so it is a narrower slice of language, shaped by its medium; moreover it seems to include some things that strain the definition of language—output logs from software, say (see the “weird tokens” below). I don’t mean to be dogmatic, but it seems useful to at least acknowledge that this text data exists in a weird relationship to language as conventionally construed.

But my more essential point is that the same way these models learn grammar and linguistic meaning, is how they are also simultaneously learning the plot of Hamlet, what makes a joke funny, and the forty-seventh digit of pi. It absorbs these things through the same stochastic processes. As such it seems better to describe them as models of culture: a culture that carries grammars and languages and genres along with it; a culture that surely maps to no existing lived community of humans—a culture whose borders and contents remain unclear so long as the training data is undisclosed; but like a culture, it mixes languages and knowledges together in ways that are not easily separable. This, I think, is fundamentally a point Ted Underwood has made; we should understand “neural models as models of culture rather than intelligence or individual language use” (Underwood).
They are made out of bias: Understanding these models as models not of language or knowledge, but of culture, touches on the vital question of LLMs and bias. Many talk about the bias that these models can learn through their training, and then propose ways of mitigating or managing it. Yet, is there a meaningful distinction in LLMs between knowledge and bias? These models are made of bias; there is a fundamental continuity between the ways that model is able to output grammatical sentences, make observations about the color of sky, and the other biases that these models contain. All were learned the same way.
These are objects to be interpreted: Looking over my points, I’m surprised at how critical I sound, because I find these objects really fascinating, and I am convinced that people like literary and cultural historians should be more and better engaged with them (and not primarily because these models may soon be authoring a portion of all term papers in humanities classes).

Understood not as intelligences but as texts, produced as all texts are—out of other texts, and made of biases that inhere in the same substrate as language, these are fascinating objects. I find myself fascinated precisely because I am impressed and I want to understand how they’re working—not just how the model was trained, tuned, and reinforced, but how can we understand exactly what is being modeled? This is something that would require us to compare a model to its training data; but of course that alone would hardly be sufficient, because the training data is simply so massive that we couldn’t read it even if we wanted. Are there ways to read the models?

The example of “weird tokens” or “glitch tokens” (well dicussed in this computerphile video, and elaborated with interesting examples here) provides one way of approaching them that is not simply chatting with them: finding strategies for examining the strange weird castings of the training data. Such an approach should not be totally alien to anyone involved in the study of texts, their interpretation, and their history. It is the inaugurating assumption of fields like literary studies that the texts we read remain strange and alien to us, worthy of additional consideration, study, and exploration. And it seems useful to find ways of adopting such an attitude to these new textual objects.

Works Cited

Bender, Emily M., et al. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, ACM, 2021, pp. 610–23, https://doi.org/10.1145/3442188.3445922.

Bender, Emily M., and Alexander Koller. “Climbing Towards NLU: On Meaning, Form, and Understanding in the Age of Data.” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2020, pp. 5185–98, https://doi.org/10.18653/v1/2020.acl-main.463.

Chiang, Ted. “ChatGPT Is a Blurry JPEG of the Web.” New Yorker, 9 Feb. 2023.

Huang, Saffron. ChatGPT and the Death of the Author. New Statesman, 26 Feb. 2023, https://www.newstatesman.com/the-weekend-essay/2023/02/chatgpt-death-author-big-tech-artificial-intelligence.

Roose, Kevin. “A Conversation With Bing’s Chatbot Left Me Deeply Unsettled.” The New York Times: Technology, 16 Feb. 2023, https://www.nytimes.com/2023/02/16/technology/bing-chatbot-microsoft-chatgpt.html.

Schmidt, Ben. You’ve Never Talked to a Language Model. Ben Schmidt: Blog, 18 Feb. 2023, https://benschmidt.org/post/2023-02-19-sydney/.

Underwood, Ted. Mapping the Latent Spaces of Culture. The Stone; the Shell, 21 Oct. 2021, https://tedunderwood.com/2021/10/21/latent-spaces-of-culture/.

Weil, Elizabeth. You Are Not a Parrot. Intelligencer, 1 Mar. 2023, https://nymag.com/intelligencer/article/ai-artificial-intelligence-chatbots-emily-m-bender.html.

Wolfram, Stephen. What Is ChatGPT Doing … and Why Does It Work? 14 Feb. 2023, https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/.

If we understand these technologies as essentially extracting, or representing, some common, social, object, then we might follow Huang in asking, “is it right that private companies which draw on the thoughts and words of the commons, then sell ads and distribute flattened opinions back to the very same public?” . In so much as we understand language, and the text of the internet, as “public things,” it is hard not to see these technologies as acts of enclosure.↩︎

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Chris Forster

The Reddit Classics

A Classic is Not a Poem

A Classic is (Probably) Russian or Victorian

A Classic is Old

A Classic is Canonical(ish)

The Classics are a Little Weird

A Classic is Not a Social Media Post

Notes

Works Cited

Assays of Bias: LLMs and Geometries of Meaning

Another Attempt to Explain how LLMs Work

Each Meaning is Utterly Unique

The Well-Wrought Vector: Uniqueness and Precision

Works Cited

On Analog Holes and Streaming Schemes

Works Cited

The Case of Cluster 4: A Data Mystery

Clustering the Three-Volume Novel

A Laborious Exercise in Hunch-Proving

Advertisement in the Time of the Triple-Decker’s Decline

Concluding Methodological Reflection

Works Cited

Zadie Smith’s The Fraud

How Long was the Three Volume Novel?

The Three-Volume Novel

Lengths

Length Over Time

Works Cited

Text, not Language: Inexpert Provocations on Language Models

Provocations

Works Cited