Preventing Child Sexual Abuse: Evidence, policy and practice
Bibliographic record
Abstract
Public policy responses to child sexual abuse are dominated by interventions designed to take effect only after offenders have already begun offending, and after children have already been sexually abused. Comparatively little attention has been given to alternative prevention strategies – particularly to those aimed at preventing sexual abuse before it might otherwise occur. Considerable knowledge has been accumulated on the characteristics, modus operandi and persistence of offenders, the characteristics, circumstances and outcomes for victims, and the physical and social settings in which sexual abuse occurs, but little work has been done to systematically apply this knowledge to prevention.This book aims to fulfill this objective through integrating clinical and criminological concepts and knowledge to inform a more comprehensive and effective public policy approach to preventing child sexual abuse. Empirical and theoretical knowledge concerning child sexual abuse is integrated with broader developments in evidence-based crime and child maltreatment prevention, leading to new ideas about understanding and preventing child sexual abuse. This book will be essential reading for anybody with interests in this field.
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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 | 0.029 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".