Trends in the Imprisonment of Women in Canada
Bibliographic record
Abstract
There are conflicting claims about whether women's imprisonment in Canada has followed the trend toward increasing punitiveness observed in a number of other western nations. This paper provides a detailed description of the scope of women's imprisonment in Canada since the early 1980s to adjudicate between these claims. Using different measures of imprisonment and data from federal and provincial prisons for women, the paper shows that we do not have convincing national evidence that there has been substantial growth in women's imprisonment Canada over the past few decades. There are however, some important gaps in the existing data that make it impossible to describe the full extent of the imprisonment of women and, more importantly, trends in the size of the population of women in prison. At the same time data from one province – Ontario – describe an important and disconcerting shift in the nature of women's imprisonment that has gone largely unnoticed by scholars: a large and growing proportion of the imprisoned female population is made up of women who are not serving sentences. The paper concludes with a call for more attention to the increase in the remand population and to what it means for theories of punitiveness.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".