Alibi believability: Corroborative evidence and contextual factors
Bibliographic record
Abstract
A disbelief in alibis is one contributor to wrongful convictions. One reason that triers-of-fact may disbelieve alibis is that they lack evidence to corroborate the whereabouts of the suspect at the time of the crime. Contextual factors, such as when the alibi was disclosed and what was the nature of the crime, can also affect alibi believability. This paper outlines two studies where mock jurors evaluated an investigation and trial description online and rated alibi believability, defendant character trait ratings, and verdicts. Both studies examined the impact of corroborative alibi evidence and the timing of the alibi disclosure. In addition, Study 1 included the type of crime and Study 2 included the number of alibi corroborators as additional independent variables. We hypothesized that alibis would be viewed more positively when they were disclosed earlier rather than later, were corroborated by strong physical evidence and multiple corroborators, and involved less violent offenses. As hypothesized, in both studies, alibis with strong physical evidence were thought to be more believable than those with no physical evidence but the number of corroborators and type of crime did not affect any dependent measures. Delayed timing had some negative effects on views of the defendant's character. Corroborative physical evidence affected alibi believability consistently, and contextual factors mattered less. Both implications and suggestions for future research are further 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 | 0.010 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".