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Record W2274543294 · doi:10.1177/0886260516632355

Racial Composition of Couples in Battered Spouse Syndrome Cases: A Look at Juror Perceptions and Decisions

2016· article· en· W2274543294 on OpenAlexaff
Annik Mossière, Evelyn M. Maeder, Emily Pica

Bibliographic record

VenueJournal of Interpersonal Violence · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsCarleton UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsSpousePsychologyHuman factors and ergonomicsSuicide preventionPoison controlPerceptionOccupational safety and healthInjury preventionMedical emergencySocial psychologyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

This study manipulated the race of the defendant (wife) and the victim (husband)-White/White, White/Black, Black/Black, and Black/White-in a murder case involving a history of intimate partner violence (IPV) to examine the potential prejudicial impact of race on juror decision-making. A total of 244 jury-eligible American community members read a trial transcript of a murder case in which the defendant claimed self-defense using evidence of battered spouse syndrome. Participants provided a verdict, responsibility attributions, and their perceptions of the scenario. Findings revealed that the Black defendant (wife) was more likely to be found not guilty by reason of self-defense, and female jurors were overall more likely to acquit the defendant (wife) than were men. These results contribute to the scarce literature on the influence of race on perceptions of legal proceedings involving IPV.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.039
GPT teacher head0.327
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2016
Admission routes1
Has abstractyes

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