What's Good for the Goose? Examining the Impact of Gender-neutral and Gender-specific Factors in the Assessment and Treatment of Female and Male Justice-involved Youth
Bibliographic record
Abstract
In response to female youths’ increased visibility in the legal system, more attention has been paid to understanding girls’ pathways to justice system involvement, risk for re-offending, and rehabilitative needs. Widely-used risk assessment and case management tools based on the Risk Need Responsivity (RNR) framework are largely gender neutral. Gender-responsive scholars have long advocated for the importance of additional gender-specific factors in guiding the assessment and treatment of female justice-involved youth. The dissertation is comprised of two papers which examine the contribution of proposed gender-specific factors alongside well established RNR factors in the prediction of recidivism, and how service provision aimed at intervening with these factors impacts recidivism for both male and female justice-involved youth. Paper 1 explores the relationship between trauma, criminogenic needs and recidivism. I first sought to define the distinct constructs often referred to under the umbrella term ‘trauma’: PTSD symptomology, maltreatment, and childhood adversity. The relationships between these factors, well-established criminogenic needs, and recidivism were examined and compared in a matched sample of 50 female and 50 male justice-involved youth. Females were significantly more likely than males to have experienced multiple forms of maltreatment. Several maltreatment and childhood adversity factors were significantly and positively related to criminogenic needs. PTSD symptomology and childhood adversity were not significant predictors of recidivism; however, maltreatment was the strongest predictor of recidivism for both males and females in a model that included well established risk factors. Gender was not found to be moderating the relationship between maltreatment and recidivism. Implications of the findings for theory and practice are discussed. Paper 2 examines the contribution of both criminogenic needs and several additional proposed ‘female’ gender-specific factors to risk assessment and rehabilitative treatment. Female youth were more likely than male youth to have proposed ‘female’ gender-specific needs but these needs alone did not predict recidivism. Successfully matching services to youths’ criminogenic needs predicted reduced recidivism for both male and female youth. For youth who had ‘female’ gender-specific needs, successful matching of services to these needs also predicted reduced recidivism for both genders. Theoretical and practice implications of these results are discussed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".