The Search for Predictable Moral Partners: Predictability and Moral (Character) Preferences
Bibliographic record
Abstract
Across six studies (N = 1,988 US residents and 81 traditional people of Papua), participants judged agents acting in sacrificial moral dilemmas. Utilitarian agents, described as opting to sacrifice a single individual for the greater good, were perceived as less predictable and less moral than deontological agents whose inaction resulted in five people being harmed. These effects generalize to a non-Western sample of the Dani people, a traditional indigenous society of Papua, and persist when controlling for homophily and notions of behavioral typicality. Notably, deontological agents are no longer morally preferred when the actions of utilitarian agents are made to seem more predictable. Lastly, we find that peoples’ lay theory of predictability is flexible and multi-faceted, but nevertheless understood and used holistically in assessing the moral character of others. On the basis of our findings, we propose that assessments of predictability play an important role when judging the morality of others.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".