Federally Sentenced Women and Security Classification
Bibliographic record
Abstract
This article stems from work I completed in 2004. It only touches on some of the many, significant issues relating to incarceration practices for federally sentenced women in Canada, and women classified as maximum security in particular. That document is available at the Simon Fraser University library, and via www.prisonjustice.ca (see references). In another part of my life I am a newly registered midwife, living and working in East Van. Thank you to the women who provided feedback on earlier drafts of this article, and updates on just how bad things continue to be, for women classified as maximum security. For women serving time in Canada’s federal prisons over the last decade, many reforms have taken place, which have altered the way their incarceration has looked from outside the prison walls. Perhaps the most significant changes have been for women classified as maximum security, who ten years ago went from being held in one, centralized prison in Ontario – Kingston’s notorious Prison for Women (P4W) – to segregated units in men’s maximum security prisons across the country. After almost ten years of these ‘temporary, ’ co-located units, where women were isolated and their movements severely restricted, the Correctional Service of Canada (CSC) has recently moved maximum-security women into “Secure Units, ” recently built at each of the regional, federal women’s prisons in the country (except the one designed
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".