Bibliographic record
Abstract
Abstract: Leading virtue epistemologists defend the view that knowledge must proceed from intellectual virtue and they understand virtues either as refned character traits cultivated by the agent over time through deliberate effort, or as reliable cognitive abilities. Philosophical situationists argue that results from empirical psychology should make us doubt that we have either sort of epistemic virtue, thereby discrediting virtue epistemology’s empirical adequacy. I evaluate this situationist challenge and outline a successor to virtue epistemology: abilism. Abilism delivers all the main benefts of virtue epistemology and is as empirically adequate as any theory in philosophy or the social sciences could hope to be. 1. Situationism and ethics Decades of research in social psychology taught us counterintuitive but valuable lessons about the determinants of human behavior. Situational factors infuence our behavior to an extent that commonsense wouldn’t predict and which is shocking upon refection (e.g. Hartshorne & May; Milgram 1974; Darley & Batson 1973). Although people’s behavior is fairly consistent over time in very similar situations, it can be highly inconsistent across situations that differ in ways that we might ordinarily think are insignifcant (Mischel & Peake 1982). Moreover, the predictive value of situational variables can exceed the predictive value of (what we take to be) a person’s traits such as honesty or generosity. Indeed the predictive value of traits can
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".