Bringing Computation into Cultural Theory: Four Good Reasons (and One Bad One)
Bibliographic record
Abstract
We used to talk. By "we" I mean cultural sociologists and scholars in the humanities, and by "used to talk" I mean acknowledge each other's existence, and at times, perhaps even generously so. There are different versions as to what happened, one of which is a bit more intellectual than the other, although neither of which are entirely right. The more intellectual version is that for a brief spell in the late 1980s and early 1990s it looked like our interests might converge. At around the same time many of us stopped being scolds about popular culture, deciding instead that it was more fruitful and interesting to engage the world than to police it. Some of us were also asking similar questions, be it about the role of authors and their ability (or lack thereof) to enforce, guide, or push readers into certain meanings, or about the role of interpretive communities and groups to either buffer against the impingement of those meanings, or to generate localized meanings all anew. So we congregated around folks like Richards, Iser, Jauss, Bakhtin, Fish, Barthes, or Foucault, and sometimes we even cited each other too, and then it just all kind of petered out.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.015 | 0.125 |
| Scholarly communication | 0.029 | 0.058 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".