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Replications and Limitations of a Two‐Factor Model of Child Witness Credibility

2003· article· en· W2051342016 on OpenAlexaff
David F. Ross, Frank H. Jurden, R. C. L. Lindsay, Jennifer M. Keeney

Bibliographic record

VenueJournal of Applied Social Psychology · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicJury Decision Making Processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsWitnessHonestyCredibilityPsychologyVerdictSocial psychologyRecreationCognitionPerceptionDevelopmental psychologyLaw

Abstract

fetched live from OpenAlex

Two experiments are reported that test the idea that jurors perceive child witnesses in terms of a 2‐factor model of credibility with the factors defined as cognitive ability and honesty (Leippe & Romanczyk, 1987; Ross, Millers, & Moran, 1989). In the first experiment, 300 mock jurors watched a realistic videotaped recreation of a sexual abuse trial and rated the credibility of the child witness. Mock jurors perceived the child witness in terms of 2 factors: cognitive ability and honesty. Only honesty predicted verdict. These findings were replicated in Experiment 2 (N= 300) when only the child's testimony was presented and the perceptions of the child witness were not contaminated by the testimony of the other witnesses in the trial.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.179
metaresearch head score (Gemma)0.371
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.946

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1790.371
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0030.006
Open science0.0060.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.166
GPT teacher head0.438
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainReproducibility
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

Citations85
Published2003
Admission routes1
Has abstractyes

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