Bibliographic record
Abstract
“I write for friends and strangers.” So writes Stanley Cavell in Little Did I Know, misquoting Gertrude Stein (who in fact wrote for herself and for strangers). Cavell long wrestled with uncertainty about how his books would be—and had been—received, with whether he could make himself understood to his readers. The friends who share his conviction that everything—art, language, autobiography—matters, and that we must try as best we can to communicate with others. The strangers whose minds are more mysterious still, but to whom he felt a duty to reach out. On the occasion of the publication of our respective books, Stanley Cavell and Film: Scepticism and Self-Reliance at the Cinema (Bloomsbury, 2019) and Stanley Cavell and The Arts: Philosophy and Popular Culture (Bloomsbury, 2020), we read one another’s work and were moved to begin a conversation. Here, we speak to each another about finding Cavell, the tricky business of interpretation and the future of Cavell studies.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".