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Record W2003644936 · doi:10.1108/20093821211264423

Gender, emotionality, and victim impact statements

2012· article· en· W2003644936 on OpenAlexaff
Kristine A. Peace, Deanna L. Forrester

Bibliographic record

VenueJournal of Criminal Psychology · 2012
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversity of LethbridgeMacEwan University
Fundersnot available
KeywordsPsychologyEmotionalityEmpathySocial psychologyVignetteCredibilityPerceptionPunishment (psychology)Developmental psychologyOriginality

Abstract

fetched live from OpenAlex

Purpose The present study aims to examine the influence of emotional content and gender pertaining to victim impact statements (VIS) on sentencing outcomes. Design/methodology/approach The authors used a 2 (emotionality)×2 (participant gender)×2 (victim gender)×2 (statement gender) factorial design. Participants ( n =715) read a crime vignette and corresponding VIS, and completed questionnaires pertaining to sentencing recommendations, legal attitudes, and levels of emotional empathy (counterbalanced). Findings Results indicated that participant gender was related to the emotional appeal of the VIS, and ratings of punishment severity. Emotional empathy was positively associated with perceptions of credibility and emotionality. Higher legal attitudes scores were positively correlated with higher minimum sentences, ratings of credibility, emotional appeal, as well as more severe punishments. Originality/value This study has important implications with respect to perceptions of VIS in relation to how emotional they are, who the victim is, who the statement is written by, and who hears the statement. Given the lack of previous research in this area, the study provides data that warrant further investigation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.677
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.157
GPT teacher head0.517
Teacher spread0.360 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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