Violence Risk Assessment in Women: The value of the Female Additional Manual
Bibliographic record
Abstract
Women and girls represent only a minority in the penitentiary system and in forensic mental health care. About 6%–10% of both prison and forensic psychiatric populations in Western countries comprise women (see for the most recent offi cial statistics in the UK w ww.gov. uk/government, in Canada w ww.statcan.gc.ca, and in the US w ww.bjs.gov) . However, there seems to be widespread agreement that in the past 20 years female offending has been on the rise, especially violent offending and particularly among young women ( Miller, Malone, and Dodge, 2010; M oretti, Catchpole, and Odgers, 2005) . Overall, a disproportionate growth of females entering the criminal justice system and forensic mental health care has been observed in many countries (for reviews, see Nicholls, Cruise, Greig, and Hinz, 2015; Odgers, Moretti, and Reppucci, 2005 ; Walmsley, 2015) . In addition, it should be noted that the ‘dark number’ for women is suggested to be bigger than for men. Offi cial prevalence rates of female offending might constitute an underestimation as women usually commit less reported offences, for example, domestic violence (N icholls, Greaves, Greig, and Moretti, 2015) . Furthermore, it has been found that – if apprehended – girls and women are treated more leniently by professionals and the criminal justice system. Generally, they receive lower prison sentences and are more often admitted to civil psychiatric institutions instead of receiving a prison sentence or mandatory forensic treatment after committing violence ( Javdani, Sadeh, and Verona, 2011 ; Jeffries, Fletcher, and Newbold, 2003 ). Hence, although female offenders compared to male offenders are a minority, female violence is a substantial problem that deserves more attention. Our understanding of female offenders is hindered by the general paucity of theoretical and empirical investigations of this population. In order to improve current treatment and assessment practices, our knowledge and understanding of female offenders should be enlarged and optimised (d e Vogel and Nicholls, 2016 ).
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".