Bibliographic record
Abstract
The National Society of Protection and Cruelty to Children (NSPCC) estimate that 1 in 20 children are a victim of sexual abuse (Bentley, O’Hagan, Raff, & Bhatti, 2016). Furthermore, recent figures indicate that there has been a rise in child sexual abuse cases across the UK. \n \nResearch into perpetrators of child sexual abuse have focused primarily on male offenders. Researchers have explored the cognitive distortions of male offenders and developed theories of sexual offending based on the risk factors associated with this population. More recently, studies have explored the core beliefs and schemas of child offenders to understand the factors that underpin their cognitive distortions. This empirical paper explores the Early Maladaptive Schemas and Implicit Theories of mentally disordered child sex offenders. \n \nHistorically, societal perspectives of women indicated that females were highly unlikely to perpetrate sexual offences against children. More recent findings suggest that females account for approximately 5% of all sexual offenders across the UK, Canada, USA, Australia and New Zealand. However, this statistic varies depending on the data gathering methods employed: self-report studies indicate higher prevalence rates of female perpetrated sexual abuse compared to case report studies. This systematic review explores societal perspectives of female child sex offenders compared to male perpetrators. \n
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".