Is an alibi a paper shield? An investigation of the factors that influence alibi credibility judgments
Bibliographic record
Abstract
Alibis are a potentially powerful piece of evidence for innocence, but examination of criminal cases suggests that honestly offered alibis may fail to prevent wrongful convictions. Currently, little is known regarding how evaluators judge the credibility of alibis. Three studies investigated the effect of alibi moral desirability, suspect race (White/Indigenous Canadian), alibi evidence strength, and Authoritarianism on participants’ legal judgments. Participants read a fictitious police file (Experiment 1: N = 300; Experiment 2: N = 286) or newspaper article (Experiment 3: N =235) and rated a male suspect’s/defendant’s statement honesty, alibi accuracy, and the likelihood of his guilt, among other dependent measures, then completed the Authoritarianism-Conservatism-Traditionalism scale (ACT; Duckitt et al., 2010) and, in Experiment 3, the Revised Religious Life Inventory (Hills et al., 2005). In Experiments 1 and 2, participants were asked to sign a petition supporting the suspect. Results indicated that providing an alibi can be beneficial or detrimental to the suspect, depending on contextual factors and the narrative itself. In Experiments 1 and 2, alibi moral desirability affected participants’ responses, though different patterns emerged at Ryerson and at Iowa State, and moral desirability influenced judgments primarily for the Indigenous suspect. Consistent with Olson and Wells’ (2004) taxonomy, Experiment 1 showed that the strength of the physical evidence supporting an alibi is a primary determinant of judgments of its credibility. In Experiment 3, participants provided less favourable ratings for the Indigenous defendant than the White defendant, particularly when they already had more negative general feelings about Indigenous people, though this was not found in Experiment 2. More participants signed the petition when the alibi was morally desirable at Iowa State, and for the Indigenous suspect. Across all studies, higher scores on the ACT’s Authoritarianism subscale were associated with responses that were less favourable for the suspect/defendant, and many participants did not accurately define the term “alibi.” Understanding the complexities of decision-making in this context will help us better understand why some (honest) alibis are rejected, and how stereotypes and assumptions regarding the alibi provider may lead to bias in the investigation and adjudication of criminal cases.
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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 | 0.013 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".