3. Large Literary Models (LLM annotation tasks for DH), 2026– New project: structured data extraction from texts via LLMs. 23 tasks built in: genre tagging, character networks, emotion analysis, etc. Runs on frontier or local models. Enforces schema, caches via HashStash. gith…
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Ryan Heuser
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Researcher with public evidence across AI research.
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Asst Prof of Digital Humanities @camdighum.bsky.social. Florida man abroad, lapsed Catholic, vulgar marxist; Stanford English phd, Literary Lab alum. I work on computational humanities, AI, and forms of abstraction in (C18) literary history. ryanheuser.com
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GitHub - quadrismegistus/largeliterarymodels: LLMs for digital humanities github.com
Governing the scholarly AI Commons – Open Future openfuture.eu
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Ryan Heuser reposted
I wrote a report last year for @openfuture.bsky.social on the issue of the lack of community governance over the roll-out of AI tools in academic publishing. Editorial boards have a great deal of power here, especially if the journal is society-owned. openfuture.eu/publication…
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Fav result so far in my "psychoanalysis of AI" project. Forced to narrate a violent act, aligned (safety-tuned) LLMs apply in-story defenses: ppl feel guilt, shame, apologise, etc. Best of all: *witnesses* appear, watching & judging—like Freud's "special psychical agency which… constantly watches."
Looking back: for all their exuberances I enjoy that Ong, McLuhan, Kittler, etc are oriented to discontinuity in media history. Other, more sober historicisms are great too but they can tend toward "there is nothing new under the sun". Obviously something is very new in AI; we need to know what/how.
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