Stereotype Threat - by Hollis Robbins - Anecdotal Value
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Stereotype Threat<br>Inference and prejudice
Hollis Robbins<br>Jul 20, 2026
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Before it was a problem, a stereotype was a solution. How do you print a piece of work at scale? The answer was to cast a solid metal plate from a page of set movable type, called a stereotype, first invented in 1725 but not commercially viable until around 1800. A print compositor would set the type, then take a plaster mold, then pour molten type-metal to make one rigid plate of the whole page. The original type was taken apart and put back into the letter case to set the next page. The first stereotyped book printed in America was a Bible, in 1812, from plates the Philadelphia Bible Society imported from England. The American Bible Society, founded 1816, made stereotypes its business model. By the 1820s half of American-made Bibles were stereotyped.<br>A stereotype makes sense with stable works (like the Bible), where you’re not likely to change a single word, and you are expecting a big print run. You can sell the print run and you can also rent or sell the plates to other printers and publishers, who can re-use them, but can’t change a word. This matters for my field (literature) because publishers decide early what to stereotype (like Uncle Tom’s Cabin) and what not to (books you’ve never heard of). Famously, Ticknor & Fields, the publishers of The Scarlet Letter (1850), did not expect it to sell quickly and did not stereotype the first run. They scrambled and set an error-filled second run, and then finally cast stereotype plates for a third edition.<br>Anecdotal Value is a reader-supported publication. To receive new posts and support my work, consider becoming a subscriber.
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Once you make a plate it isn’t useful for anything else except printing that page of text with ink and a fresh piece of paper. Newspapers, which cast fresh plates daily, melted them down at the end of the day (unless it was an important day). Book authors sometimes asked for their plates when a publisher was done printing (and the book wasn’t going to be a runaway best seller).<br>Today you read headlines about hundreds of billions of dollars being spent a year on inference chips, processors that do one thing, usually running a trained large language model (LLM). Google is reportedly developing a chip, nicely called Frozen v2, that would permanently etch Gemini’s computing logic onto the silicon wafer, “akin to ‘freezing’ the software in the chip.” Is it a leap to compare an inference chip to a stereotype? No, in fact. The inference chip holds the finished model as a mass of frozen numbers, called weights.<br>AI models read and write in “tokens,” which follow similar principles to a type case, which was weighted toward which letters are most commonly used. A type case holds far more e’s than z’s, more vowels than consonants. You need letters in the proportions they appear. When movable type was set by hand, the most common letters were close at hand. Frequent pairs and words came pre-cast as single pieces — fi, fl, and the “&.” A token set is built the same way: a large corpus is scanned, the character sequences that appear together most often are merged into single units, common whole words become one token (like “and” was one sort), and rare words are assembled from smaller pieces. The model then generates one token at a time, predicting each from the likelihood of what follows what.<br>An LLM is one enormous formula for predicting the next token, and the weights are its settings: hundreds of billions of numbers whose values, in combination, determine what the model writes. A model is trained by scanning a corpus that includes just about everything written often enough to survive, which is why so many stereotyped texts are represented in the corpus.<br>Training adjusts the model’s prediction toward what texts in the corpus tend to say. When training stops, the weights are fixed, somewhat similar to the way a page of type is fixed before the molten metal is poured to make a stereotype. The model reads the fixed numbers from memory and uses them to process a response to a prompt, without a mechanism to change a weight. With a stereotype, every fresh sheet rolled against it looks the same. With an LLM, the answers vary with the prompt, but the same assumptions (weights) are operating. Everyone will get a similar answer to a similar question.<br>Some differences. First, the weights are a file, and a file can be copied exactly and endlessly, so millions of users can use it. Second, in this analogy the weights are the stereotype plate and the chip is the press. When a company retrains its model, it’s a new stereotype plate. Neither the stereotype plate nor the chip is “corrected” by editing at the margins. The Bible Society’s “Directions for Repairing Plates” describes physically working the metal, like an auto shop getting rid of a dent. If there is an error in a trained...