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Record W3041883591 · doi:10.1002/bsl.2473

Alibi believability: Corroborative evidence and contextual factors

2020· article· en· W3041883591 on OpenAlexaff
Meredith Allison, Sandy Jung, Amanda Benjamin

Bibliographic record

VenueBehavioral Sciences & the Law · 2020
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMacEwan University
FundersElon University
KeywordsAlibiAffect (linguistics)PsychologySuspectSocial psychologyCharacter traitsCriminologyCommunication

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.320
GPT teacher head0.434
Teacher spread0.114 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2020
Admission routes1
Has abstractyes

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