‘Notes on a Scandal’: Why do Females Engage in Abuse of Trust Behaviours?
Bibliographic record
Abstract
Although an evident reality in organisations where females work with young people, there has been no specific research to date exploring why females in positions of trust engage in sexually abusive relationships with adolescents. This study investigated the subject through a qualitative analysis of ten case studies from England drawn from the employment and safeguarding environment, comparing findings with existing studies into female sexual offenders in general, research into male ‘professional perpetrators’ and Gannon et al.’s (2008) Descriptive Model of Female Sexual Offending. The research highlighted a number of key similarities and differences between those who abuse in positions of trust and those female sexual offenders who abuse children in wider contexts. With respect to etiological factors the similarities included unstable lifestyle, relationship difficulties, low self-esteem, cognitive distortions and emotional self-management problems. Motivations for this sample appeared to be primarily driven by intimacy needs. Differences were identified relating to lower levels of substance abuse, a higher age range and socio-economic status, less prevalence of severe social skills deficits and chaotic and abusive backgrounds in this subject group. All of the women in the study followed an Implicit Disorganised pathway of abuse and maternal approach to the abusive behaviour.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".