Donald Davidson: Looking Back, Looking Forward (Volume Introduction)
Bibliographic record
Abstract
The papers collected in this issue were solicited to celebrate the hundredth anniversary of Donald Davidson’s birth. Four of them discuss the implications of Davidson’s views—in particular, his later views on triangulation—for questions that are still very much at the centre of current debates. These are, first, the question whether Saul Kripke’s doubts about meaning and rule-following can be answered without making concessions to the sceptic or to the quietist; second, the question whether a way can be found to answer Davidson’s own doubts about the continuity of non-propositional thought and language; third, the question whether normative properties can be at once causal and prescriptive; fourth, the question whether folk psychological explanations can be at once illuminating and autonomous. The fifth paper reexamines Davidson’s take on the principle of compositionality, which always was at the centre of his theorizing about language.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".