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Record W4245386949 · doi:10.1375/1321871042707304

The Impact of Perpetrator Gender on Male and Female Police Officers' Perceptions of Child Sexual Abuse

2004· article· en· W4245386949 on OpenAlexaboutno aff
Donna M. Kite, Graham Tyson

Bibliographic record

VenuePsychiatry Psychology and Law · 2004
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsSeriousnessVignetteChild sexual abusePsychologyOfficerPerceptionSexual abuseSocial psychologyClinical psychologyPoison controlSuicide preventionMedicinePolitical science

Abstract

fetched live from OpenAlex

Research in America, Canada and England indicates that professionals involved in the investigation of child sexual abuse cases have differing perceptions of seriousness, punishment and impact on the child, based on the professional's gender and the perpetrator's gender. The aim of this study was to investigate if such gender effects are prevalent in Australian child-abuse investigators, specifically the police. To assess this, 361 Australian police officers responded to a self-report questionnaire relating to a vignette describing child sexual abuse. The questions examined the police officer's perception of seriousness of the incident, the police action they would take and the perceived impact on the child. The vignette described the perpetrator as either male or female, with 172 police officers responding to the female perpetrator vignette and 189 responding to the male perpetrator vignette. The results indicated that, unlike overseas research findings in this area, the police officers' gender did not influence their perception of child sexual abuse, their perceived impact on the child, or the police action they would take. The gender of the perpetrator did however influence these factors, with a gender bias in favour of the female perpetrator. This finding is consistent with overseas research and is a factor that those working in the area should be aware of to ensure incidents involving female perpetrators are not underestimated or dismissed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.736

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.331
Teacher spread0.313 · 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.

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

Citations1
Published2004
Admission routes1
Has abstractyes

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