Bibliographic record
Abstract
A range of phenomena - from wrongful convictions to family vendettas – suggest that blame and punishment sometimes target individuals who are not involved in wrongful acts. However, dominant causal theories of blame often fail to explain this. In the present research we go back to Heider’s (1958) proposal that association is the first, most-basic mechanism for attribution of responsibility, examining whether the existence of social association on its own could allow for blame to spread. We presented 101 university students with stories featuring a character who was in the same group as the perpetrator of a harmful deed and a character who was not. Both characters were depicted as uninvolved and unaware of the harm. A third group member was an accomplice of the perpetrator. Groups were either presented as being close knit (high in entitativity) or organized ad-hoc (low in entitativity), allowing us to examine how strength of affiliations influence on the spread of blame. Participants rated the victim's anger towards each character. Our key hypotheses posited that (a) uninvolved group members would incur more blame for a harmful act than uninvolved non-group members, (b) this difference in blame attribution would be greater in groups with high entitativity. As expected, the accomplice was blamed less than the perpetrator, and the group member and the non-group member were blamed less than the accomplice. However, consistent with the associative proposal advanced by Heider, participants attributed more blame to the uninvolved group member than the equally uninvolved non-group member. The difference was significant in the high but not in the low entitativity condition. These findings provide strong evidence for the role of non-causal social association in the spread of blame, calling for the expansion of current models of blame attribution to incorporate purely associative pathways.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".