Development and Field Test of a Gender-Informed Security Reclassification Scale for Female Offenders
Bibliographic record
Abstract
Two samples of Canadian federal female offender case files are used to develop and test a gender-informed security reclassification scale. Study 1 uses 285 consecutive offender security level (OSL) reviews for federally sentenced women to empirically construct the Security Reclassification Scale for Women (SRSW). Study 2 uses all federal female OSL reviews that occurred between July 2000 and June 2003 ( n = 580) to test the validity and reliability of the SRSW. Results suggest that the SRSW is a reliable and valid tool for the security classification of federally sentenced women in Canada. Relative to the current classification method, the SRSW places fewer cases at maximum security and more cases at minimum security. Within a fixed 3-month follow-up, the SRSW is significantly more predictive of minor institutional misconduct than the structured clinical method currently in use. Results are discussed in terms of both theoretical and operational implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".