Bibliographic record
Abstract
Drawing on findings from prison inquiries and commissions, correctional policies and practices, news media accounts and prisoner testimonies, this article reconsiders the prevalence of violence against women in Canadian prisons. By adopting a broader conceptualization of violence, particularly as enacted and legitimated by the state, the article reveals how the prison setting generates a landscape of violence through the languages of gender and security, along with routine practices that come together to facilitate and heighten the conditions for violence to occur in seemingly, normal, benign, and necessary ways. This can include high risk designations, higher classifications, involuntary and forced transfers, deportation, strip-searches, administrative segregation, suicide watches, dry cells, transfers to men’s prisons, lack of medical attention, and the denial of support or service – all of which are often experienced in violent ways and can culminate into self-harm, suicide, and death. The more violent aspects of the prison system are made less visible through routine practices and a normal politics that minimize, obscure, or ignore violence. Given its inherently violent character, we have to question the use, necessity, and relevance of incarceration to enact justice.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.030 | 0.041 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".