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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".