Why are Women Canada's Fastest-Growing Prison Population and Why Should We Care?
Bibliographic record
Abstract
When I first started work with Elizabeth Fry, I actually believed that there was not much difference between the circumstances of men and women prisoners. It only took a couple of months of being in the job to realize how wrong I was. Women's histories of neglect and abuse, poverty, motherhood, isolation and dislocation, and the overwhelming realization that they really were too often simply considered “too few to count” brought their circumstances into sharp relief, and I soon realized that the landscape of women's criminalization and imprisonment stood in stark contrast to any of my preconceived notions. The women and this work have educated and activated me in many ways, as we have journeyed many bumpy and seemingly obscure paths together. One such seemingly impassable journey and also one of life's turning points for me started on 28 April 1994, the day that I went into the Prison for Women (P4W) in Kingston aft er the emergency response team had stripped and shackled several women and left them naked or dressed with only a flimsy paper “gown” in the segregation unit. At the end of that long day, when I advocated that they unshackle the one woman who was still restrained and release from segregation all eight of the other women, I was advised that I was misinformed about the circumstances and treatment of the women and that, in fact, there were no women in restraints. When I insisted that I had actually observed the shackles, it was suggested by staff that perhaps it was a reflection from the bars. And when I persisted, I was counselled against being so easily “conned” by the women. As I exited P4W that evening with my then three-and-a-half-year-old son, I remember standing on the steps and realizing that they must believe that this information would never emerge and that if it did, no one would ever believe it. On that day, I thought, I don‘t know exactly how to do this, I don ‘ t know how one comes up against a system that has all of the resources and a full government department of lawyers to assist them in that process, but I had better figure out how.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".