Bibliographic record
Abstract
Stanley Cavell roams across a wide range of fields in his first book, Must We Mean What We Say? most obviously those of epistemology, ethics, and aesthetics. But nowhere in the book’s ten essays does he advance an explicit political theory. Still, this book, published in 1969 and written over the course of the preceding decade, quietly poses persistent political questions, even in essays on such topics as skepticism and King Lear, Kierkegaard’s Book on Adler and Beckett’s Endgame, atonal music and ordinary language philosophy. Just who is the “we” spoken of in the book’s title (we philosophers? we Americans? we human beings?)? Is there any relationship between democratic equality and the philosophical appeal to our everyday language, as described in the book’s eponymous essay? 1 Does the account that Cavell offers in his piece on Wittgenstein of practices and behaviors shared across cultures—the “whirl of organism” of our forms of life—suggest a nascent theory of human solidarity? Our freedom in language and the responsibility we bear for meaning, topics of the book’s opening essays, raise the question of what we might owe to one another and how we might offer—or withhold—it in our choices of words. Is this the beginning of a theory of justice? The concept of acknowledgment, described in the book’s final essays as a response to the challenge of skepticism, shifts the problem from what I can know to what I might do. Is this a theory of moral or political action (or both)?
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.218 | 0.170 |
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".