Gender Matters: An Introduction to the Special Issues on Women and Girls
Bibliographic record
Abstract
Girls and women represent a minority of forensic mental health and prison populations. However, studies worldwide suggest that there has been a steady increase in the number of girls and women being managed by forensic mental health services and correctional agencies over the past two decades. The vast majority of research in the forensic mental health field, however, remains steadfastly focused on male populations. As such, there are growing concerns about whether the theoretical knowledge we have on male offenders is sufficiently valid and useful for female offenders. There remain substantial gaps in knowledge and debate regarding the importance of gender differences, for instance, in developmental pathways to offending and in violence risk factors and assessment. There is a similar paucity of knowledge on the efficacy of treatment in female offenders and a need for treatment programs that are specifically responsive to the needs and issues of these girls and women. These special issues of the International Journal of Forensic Mental Health dedicated to gender issues in the forensic field marks a substantial effort to enlarge the empirical and theoretical knowledge on (violent) offending, assessment, and treatment in girls and women. In this introduction article, we aim to highlight the relevance of studying gender differences in the forensic field and to provide a brief overview of important gender issues in developmental pathways to offending, gender differences and similarities in the nature of offending, assessment and treatment in forensic mental health care and the criminal justice system. Finally, we provide recommendations for practitioners, researchers, and policymakers to move forward on this topic in the forensic 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".