Bringing Computation into Cultural Theory: Four Good Reasons (and One Bad One)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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, 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 either to buffer against the impingement of those meanings or to generate localized meanings all anew. So we congregated around folks such as I. A. Richards, Wolfgang Iser, Hans Robert Jauss, Mikhail Bakhtin, Stanley Fish, Roland Barthes, or Michel Foucault, and sometimes we even cited each other too, and then it just all kind of petered out.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it