Female sexual offenders and judicial decision-making
Bibliographic record
Abstract
Background: Research examining female sexual offending is limited, and the profile of this unique offender group is not well understood. Female sexual offending has largely been identified as a rare occurrence, typically perpetrated in the context of an unhealthy relationship with a paraphilic male counterpart. Given recent changes in law and minimum sentences for sexual offences—particularly in relation to child pornography and child luring offences—it remains unknown how this has impacted sentencing of female offenders charged with sexual offences. Objectives: The goal of this study is to better understand female offenders convicted of sexual offences and their offence characteristics. Additionally, it seeks to identify patterns in judges’ decision-making with respect to aggravating and mitigating factors that impact sentencing decisions.Methods: A sample of 26 judges’ sentencing decisions between 2000 and 2017 were obtained to investigate the Canadian female sexual offender (FSO) as she enters the justice system. Results: In the study sample, high rates of psychopathology and childhood trauma were important features of this offender group. Offences tended to occur over extended periods, with a male co-offender, and with a young victim that was well known to the offender. Aggravating factors related to the vulnerability of the victim, abuse of power, and lack of insight. Mitigating factors related to accepting responsibility and a desire for self-change. Offenders were generally sentenced for two to five years, with ancillary orders intended to track and restrict further offending, rather than foster rehabilitation. Discussion: Gaining a better understanding of Canadian FSO population is the first step toward improving rehabilitation and prevention.
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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 | 0.003 | 0.020 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".