Your alibi better not be a-changin’: the effect of alibi change and interview strategy on perceptions of alibi witness’s credibility, suspect innocence, and interview quality
Bibliographic record
Abstract
Across three experiments, we assessed the effect of change in an alibi witness’ account and interviewer’s strategy on perceptions of alibi witness’ credibility, suspect innocence, and interview quality. Participants listened to a mock-interview with an alibi witness who, as the interview progressed, either altered or maintained their alibi statements in response to an interviewer’s implicit threat (Experiments 1-3), explanation of how memory works (Experiments 1-3), explicit threat (Experiments 2 & 3), or no attempt to influence the alibi witness’s account (i.e. control condition, Experiments 2 & 3). A mini-meta-analysis showed that changes in the alibi witness’ account negatively impacted ratings of suspect innocence (Md = −1.21) and alibi witness credibility (Md = -.79). The effect of changes in an alibi witness’s statement as a function of interview strategy was largest for the control (Md = −0.65) and implicit threat (Md = −0.65) conditions, followed by the explicit threat (Md = −0.51), and memory-based explanations (Md = −0.42). The implications of these findings for alibi witnesses, suspects, and criminal investigations are discussed.
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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 | 0.034 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".