When Do Observers Deprioritize Due Process for the Perpetrator and Prioritize Safety for the Victim in Response to Information-Poor Allegations of Harm?
Bibliographic record
Abstract
We examined how observers assess information-poor allegations of harm (e.g., “my word against yours” cases), in which the outcomes of procedurally fair investigations may favor the alleged perpetrator because the evidentiary standards are unmet. Yet this lack of evidence does not mean no harm occurred, and some observers may be charged with deciding whether the allegation is actionable within a collective. On the basis of theories of moral typecasting, procedural justice, and uncertainty management, we hypothesized that observers would be more likely to prioritize the victim’s safety (vs. to prioritize due process for the perpetrator) and view the allegation as actionable when the victim-alleged perpetrator dyad members exhibit features that align with stereotypes of victims and perpetrators. We supported our hypothesis with four studies using various contexts, sources of perceived prototypicality, due-process prioritization, and samples (students from New Zealand, Ns = 137 and 114; Mechanical Turk workers from the United States; Ns = 260 and 336).
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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 | 0.017 | 0.112 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".