How strongly do moral character inferences predict forecasts of the future? Testing the moderating roles of transgressor age, implicit personality theories, and belief in karma
Bibliographic record
Abstract
Three studies (total N = 1486) investigated how inferences about a person's current moral character guide forecasts about that person's future moral character and future misfortunes, and tested several plausible moderating variables. Inferences about current moral character related (very strongly) to forecasts about future moral character and also (less strongly) to forecasts about future misfortunes. These relationships were moderated by two variables: Relations between inferences and forecasts were somewhat weaker when perceivers made judgments about children, compared to judgments about adults, and relations between character inferences and forecasts about misfortunes were somewhat stronger among perceivers who more strongly believed in karma. In contrast, results provided no evidence of any moderating effects due to perceivers' beliefs about the stability of moral dispositions (i.e., implicit personality theories). These results show how dispositional inferences, moral judgments, and beliefs about karmic justice interact to shape forecasts about the future.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".