Would You Lie For Me? : Alibi Corroboration Between Strangers And Non-Strangers
Bibliographic record
Abstract
To test the assumption that individuals who share a personal relationship are more likely to corroborate one another's false alibi than are strangers, 81 undergraduate students were provided the opportunity to either corroborate or refute a confederate's alibi for a suspected theft. In a 'friendship' condition, feelings of affiliation between the participant and the confederate were experimentally induced by increasing the perceived similarity between the pair, and by having the pair interact during a collaborative task. Later during the experimental session the confederate became a suspect for a mock crime and provided a false alibi that she was with the participant during the entire session. Contrary to what we hypothesized, participants in the 'stranger' condition were as likely to corroborate the false alibi as those who underwent friendship-enhancing activities. When the confederate acted in a highly suspicious manner, however, she was much less likely to have her false alibi corroborated by participant than when the confederate's behaviour was less suspicious. The results put into question our assumptions of what makes a credible witness and emphasizes the need for further empirical research on the behaviour of alibi corroboration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.051 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".