False alibi corroboration: Witnesses lie for suspects who seem innocent, whether they like them or not.
Bibliographic record
Abstract
To test the commonly held assumption that individuals who share a personal relationship are more likely to lie for one another than are strangers, 81 undergraduate students were given the opportunity to either corroborate or refute a confederate's alibi. In either a "friendship-enhancing" or a "stranger-maintaining" condition, confederate-participant pairs completed tasks under the pretext of a problem-solving study. During the experimental session, the confederate briefly left the testing room; upon her return she either came back empty handed (evidence absent) or with money in her hands (evidence present). Later, both the confederate and participant were questioned about a purported theft in an adjacent room. When questioned by the experimenter in the presence of the participant, the confederate provided a false alibi that she was in the testing room with the participant the entire time. The experimenter later questioned the participant alone and asked whether the confederate's statement was in fact true. Although we hypothesized that participants in the friendship-enhancing condition would corroborate the false alibi more often than those in the stranger-maintaining condition, participants in both conditions were as likely to support the alibi. In the "evidence-present" condition, however, participants were much less likely to corroborate the false alibi than in the "evidence-absent" condition. The results call into question our belief that closeness and affinity toward a suspect is important in judging the truthfulness of witness statements and emphasize the need for further empirical research on alibi corroboration. The research described also introduces a new and effective paradigm to directly measure false alibi corroboration.
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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 | 0.023 | 0.173 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".