Clarification Regarding Marshall and Yates’s Critique of “Dosage of Treatment to Sexual Offenders: Are We Overprescribing?”
Bibliographic record
Abstract
The present article includes a response to recent criticisms leveled against an earlier article titled “Dosage of Treatment to Sexual Offenders: AreWe Overprescribing?” In that article, the authors argued that sexual offenders receiving low-intensity sex offender treatment in the Ontario Region of Correctional Service of Canada (CSC) may be receiving too many sex offender treatment programs. Marshall and Yates have argued that the analysis performed in that study was inappropriate and that, based on their re-analysis, the opposite conclusion may be more accurate. The present article discusses some of the assumptionsmade to Marshall and Yates in their article and presents recidivism data for individuals included in the earlier article. As well, the authors discuss additional information regarding treatment in the Ontario Region of CSC. The present article concludes that the points made in the earlier study were justified.
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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 | 0.065 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.026 | 0.045 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".