Bibliographic record
Abstract
Compatibilism is the view that moral responsibility is compatible with determinism. Natural compatibilism is the view that in ordinary social cognition, people are compatibilists. Researchers have recently debated whether natural compatibilism is true. This paper presents six experiments (N = 909) that advance this debate. The results provide the best evidence to date for natural compatibilism, avoiding the main methodological problems faced by previous work supporting the view. In response to simple scenarios about familiar activities, people judged that agents had moral responsibilities to perform actions that they were unable to perform (Experiment 1), were morally responsible for unavoidable outcomes (Experiment 2), were to blame for unavoidable outcomes (Experiments 3-4), deserved blame for unavoidable outcomes (Experiment 5), and should suffer consequences for unavoidable outcomes (Experiment 6). These findings advance our understanding of moral psychology and philosophical debates that depend partly on patterns in commonsense morality.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".