Bibliographic record
Abstract
Abstract Many philosophers hold that if an agent acts intentionally, she must know what she is doing. Although the scholarly consensus for many years was to reject the thesis in light of presumed counterexamples by Donald Davidson, several scholars have recently argued that attention to aspectual distinctions and the practical nature of this knowledge shows that these counterexamples fail. In this paper I defend a new objection against the thesis, one modelled after Timothy Williamson’s anti-luminosity argument. Since this argument relies on general principles about the nature of knowledge rather than on intuitions about fringe cases, the recent responses that have been given to defuse the force of Davidson’s objection are silent against it. Moreover, the argument suggests that even weaker theses connecting practical entities (e.g. basic actions, intentions, attempts, etc.) with knowledge are also false. Recent defenders of the thesis that there is a necessary connection between knowledge and intentional action are motivated by the insight that this connection is non-accidental. I close with a positive proposal to account for the non-accidentality of this link without appeal to necessary connections by drawing an extended analogy between practical and perceptual knowledge.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.045 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".