Bibliographic record
Abstract
Hegel is not an author who plays a starring role in Cavell’s work like that of Austin, Wittgenstein, or Emerson. Cavell mentions him rarely, and almost always in passing. This is hardly surprising. Given that Cavell draws as heavily as he does upon Kant, whom Hegel regularly attacks, and Kierkegaard, who regularly attacks Hegel, one might expect that Hegel’s more important claims and ideas would be uncongenial to Cavell, and incompatible with the main lines of his work. Moreover, Cavell’s early and lasting embrace of Romanticism would seem to preclude the embrace of an author who lambasts the leading Jena Romantic Friedrich von Schlegel as the purveyor of a corrosive amoral subjectivism. Appearances, however, can be deceiving, and in the essay that follows I demonstrate that there are good reasons to believe that Hegel has influenced Cavell considerably more than one might suppose.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".