Reform to Abolish: A Pragmatic Analysis of Prison Labor & Strip Searches in Quebec Correctional Law
Bibliographic record
Abstract
In the past year, abolitionist themes have been at the forefront of mobilizations such as #BlackLivesMatter, #MeToo, and #FreeThemAll. This paper relies on socio-legal feminist methodology and proposes a pragmatic abolitionist analysis of correctional law, the Loi sur le système correctionnel du Québec (LSCQ). I emphasize two issues reflective of ongoing structural harms within women’s jails – prison labor and strip searches – and argue that both practices instill everyday bodily harms due to their framing in the LSCQ. Although prison labor is presented as favoring social reinsertion, per the LSCQ incarcerated women receive inadequate wages relative to the cost of living in prison thus limiting their access to menstrual products and potentially leading to dangerous alternatives. As for strip searches, they are presented as means to ensure the safety of the institution yet are experienced as unsafe and as state-inflicted sexual assault. Per the LSCQ, strip searches can be conducted in a range of circumstances leaving much to correctional officers’ discretion, thus allowing for discriminatory rule enforcement and exposing incarcerated women of color to further violence. I conclude by presenting short-term abolitionist reforms which could reduce these everyday bodily harms. I also call for increased solidarity with incarcerated people within social mobilizing and organizing.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.034 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".