Weighing Outcome vs. Intent Across Societies_preprint
Bibliographic record
Abstract
Mental state reasoning has been theorized as a core feature of how we navigate our social worlds, and as especially vital to moral reasoning. Judgments of moral wrong-doing and punish-worthiness often hinge upon evaluations of the perpetrator’s mental states. In two studies, we examine how differences in cultural conceptions about how one should think about others’ minds influence the relative importance of intent vs. outcome in moral judgments. We recruit participation from three societies, differing in emphasis on mental state reasoning: Indigenous iTaukei Fijians from Yasawa Island (Yasawans) who normatively avoid mental state inference in favor of focus on relationships and consequences of actions; Indo-Fijians who normatively emphasize relationships but do not avoid mental state inference; and North Americans who emphasize individual autonomy and interpreting others’ behaviors as the direct result of mental states. In study 1, Yasawan participants placed more emphasis on outcome than Indo-Fijians or North Americans by judging accidents more harshly than failed attempts. Study 2 tested whether underlying differences in the salience of mental states drives study 1 effects by inducing Yasawan and North American participants to think about thoughts vs. actions before making moral judgments. When induced to think about thoughts, Yasawan participants shifted to judge failed attempts more harshly than accidents. Results suggest that culturally-transmitted concepts about how to interpret the social world shape patterns of moral judgments, possibly via mental state inference.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".