Policing Criminological Knowledge on Imprisonment in Pandemic Times: Confronting Opacity and Navigating Corporatization in Prison Research
Bibliographic record
Abstract
Thousands of prisoners and prison staff have been infected by COVID-19 across Canada. Deteriorating conditions of confinement have become commonplace, with segregation-like measures imposed in the name of preventing COVID-19 transmission. While prisoners, their loved ones, advocates, and researchers have discussed trends regarding infection, public health restrictions, and even vaccination behind bars, less explored is the deterioration of government transparency related to incarceration during this pandemic. Engaging with literatures on the policing of criminological knowledge, access to information, and state corporatization, this article examines how Canadian government authorities have limited access to records about imprisonment during the pandemic. We examine how the recent centralization of freedom of information request processing, which reshapes government services to mirror corporate entities, has altered what can be known about penitentiary, prison, and jail policies, practices, and outcomes. In so doing, we highlight the need for social science researchers to contest information blockades and create pathways to promote state transparency.
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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 | 0.050 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.031 | 0.078 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".