The Gendered Nature of Criminogenic Thinking Patterns Among Justice-Involved Clients: A Pilot Study
Bibliographic record
Abstract
Because much of our understanding of criminogenic thinking (antisocial cognitions) has been based on male justice populations, questions remain about the applicability of this construct to justice-involved women. Based on an item-level analysis of 216 justice-involved clients, results of this pilot study suggest that criminogenic thinking in women is relevant, and both overlaps with and diverges from that of men. In fact, the predictive accuracy for rearrest attained with a gender-responsive model developed for women exceeded that of the corresponding model developed for men (area under the curve [AUC] = .86 vs. AUC = .67). We recommend the creation of parsimonious criminogenic thinking instruments that optimize predictive criterion validity. Gender-responsive scales that capture the gender-specificity that exists in criminogenic thinking patterns can assist in (a) optimizing the prediction of reoffending and (b) identifying essential constellations of treatment targets among forensic populations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".