Bibliographic record
Abstract
Abstract For the nonreductive physicalist, behavioural effects have a complete physiological explanation and a distinct psychological explanation. In a series of papersJaegwonKim argues that there can be no more than a single complete and independent explanation of any one event, thereby excluding the psychological explanation. For his own part,Kim includes psychological explanations through the use of an extensional model of explanatory individuation. Numerous critics have pointed out the counterintuitive results of this extensional model of explanatory individuation. In a recent article in this journal,NeilCampbell suggests thatKim's property exemplification account of events provides the conceptual resources to dodge this objection. In this article I argue that this appeal to the property exemplification account does not work in the crucial case of mental explanation.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.064 | 0.086 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".