III. Revisiting Effective Classification Strategies for Women Offenders in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Psychological researchers in the western world have now provided unequivocal evidence that many correctional interventions reduce re-offending behaviour (Andrews et al., 1999; Lipsey, 1995). Losel’s (1995) review and synthesis of meta-analyses on the efficacy of correctional treatment concluded that the mean treatment effect, over all available studies, is about a 10% reduction in recidivism. Moreover, a study by Andrews et al. (1990) concluded that interventions, which focused on particular variables (e.g. risk, need) showed on average an impressive 30 percent reduction in recidivism for treated groups over those interventions that had no such focus. The seminal work by Andrews and colleagues set the foundation for casebased classification as an essential component of effective correctional treatment in Canada. There is over a decade of empirical research originating in Canada substantiating the principles of risk and need (Andrews, 1989; Andrews et al., 1999; Gendreau, 1996), so that these principles have been accepted into routine practice in treatment planning and delivery within many correctional systems worldwide. However, this research is derived, almost without exception, from samples of male (white) offenders. As such, the question remains: How does appropriate classification for women differ from appropriate classification in general? This question guided the current discussion, with an explicit focus on the principles of risk and need.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it