“That’s the way my Wednesdays always go”: reverse-order instructions insufficient to mitigate schema-consistent errors in alibi generation
Bibliographic record
Abstract
Purpose The purpose of this study was to assess the ability of innocent suspects to produce accurate alibis, as well as to identify procedures police interviewers can use to increase the probability of generating accurate alibis. Design/methodology/approach In Study 1, 54 university students had a lecture (target event) end at either the normal time (schema group) or 25 min early (non-schema group) and then attempted to generate an alibi for the target event after either a short, moderate or long delay. In Study 2, 20 students had a lecture end 25 min early and underwent an interview regarding their whereabouts using a reverse-order interview technique designed to disrupt schema usage. Findings Results from Study 1 suggested that participants relied on schemas to generate their alibis, which led to false alibis for the non-schema group, and this reliance was more pronounced as the delay between event and recall increased. In Study 2, all but one participant produced a false alibi, suggesting reverse order is ineffective in increasing accurate recall in alibi situations. Practical implications Results from the two studies revealed that people can produce false alibis easily in mock police interviews – a finding that appears to result from the reliance on schemas. These findings highlight the relative ease with which innocent individuals can produce false alibis. Further research, specific to the alibi generation process, is needed to give police interviewers the tools to produce more accurate and detailed alibis. Originality/value This research provides additional evidence regarding the role of schemas in alibi generation. Contrary to findings from the eyewitness area, reverse-order instructions failed to disrupt schema reliance and do not appear to be an effective alibi-elicitation technique.
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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 | 0.027 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".