Bibliographic record
Abstract
Abstract Ausonio Marras has argued that Jaegwon Kim's principle of explanatory exclusion depends on an implausibly strong interpretation of explanatory realism that should be rejected because it leads to an extensional criterion of individuation for explanations. I examine the role explanatory realism plays in Kim's justification for the exclusion principle and explore two ways in which Kim can respond to Marras's criticism. The first involves separating criteria for explanatory truth from questions of explanatory adequacy, while the second appeals to Kim's fine‐grained theory of events. I argue that the first response is unconvincing on its own but when coupled with the second might provide a viable way for Kim to avoid Marras's criticism. However, I show that the second strategy is weak from a polemical point of view because Kim's theory of events already assumes what the principle of explanatory exclusion was introduced to establish: the falsity of nonreductive physicalism.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".