The Impact of Perpetrator Gender on Male and Female Police Officers' Perceptions of Child Sexual Abuse
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".