Bibliographic record
Abstract
Abstract Witnessing the suffering of innocent victims can motivate observers to interpret the situation in ways that justify that suffering, such as viewing victims as more personally responsible or possessing negative traits. In a pre‐registered cross‐cultural experiment (N = 831 from India, Singapore and the USA), we tested whether belief in karma—a supernatural force that can be used to explain current misfortune as payback for past misdeeds—affects people's tendencies to blame victims for their misfortune. Participants read and evaluated descriptions of ostensibly innocent victims of misfortune, both before and after thinking about karma. When thinking about karma, participants rated victims as possessing more negative traits, and (in the USA) being less similar to participants themselves, compared to their baseline judgements. Belief in karma also indirectly predicted negative evaluations, due to karma believers' greater perception that victims were personally responsible for their situation. These results are consistent with previously established patterns of victim derogation and show how karma can shape social judgements in a manner that bolsters the perception of a just world where bad things are believed to happen to bad people.
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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 | 0.014 |
| 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 | 0.002 | 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".