Encountering Cavell in the College Classroom
Bibliographic record
Abstract
When I received the invitation from David LaRocca to contribute to this special issue of Conversations, to commemorate and celebrate Stanley Cavell’s life and thought, I felt flummoxed, overwhelmed by the possibilities. There are so many different reasons I feel gratitude, deep gratitude, for Stanley, so many ways his writings and voice have left a profound mark on my intellectual development and career and even daily life. What text or moment or effect should I single out? Where to begin? Indeed, if I had not stumbled across Must We Mean What We Say? three years into graduate school, despairing, as I was at that time, of ever feeling at home in the academic world of literary studies (this was in the late ’90s in the English Department at the University of Pennsylvania, where New Historicism was very much enjoying its heyday), I think there’s a good chance that I would never have finished my Ph.D. I had great respect for my teachers and peers, but as hard as I tried (and I did try very hard; after all, it felt like the very possibility of a career was at stake), I could not see myself reflected in their scholarly interests or outlooks.
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.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".