Degradation During Emergencies: How the Pandemic Facilitated a State of Exception Within Canadian Prisons and Challenged Advocates to Become Hyper-Resilient
Bibliographic record
Abstract
Prisoner rights in Canada have historically been met with disrespect and disregard. Advocates have continuously fought for better protections of prisoner rights and legislation that bans the harmful treatment of prisoners. However, during the outbreak of the COVID-19 pandemic concerns arose about how prisoners’ rights were affected. Therefore, the question guiding this research is: How have the human rights of prisoners and (anti) carceral advocacy for their rights been impacted by the COVID-19 pandemic? To answer this question, a document analysis was conducted, using documents from academics who work in the field, government departments, and advocates working in a variety of areas. This thesis fills the gap in scholarly inquiry that the pandemic has created as the circumstances and the effects of the pandemic are unknown. The government reacted to the pandemic by implementing protocols that suited them with little regard for how prisoner rights could be affected, and neglected advocates recommendations for change. Advocates reacted by shifting their strategies to ensure they could continue advocating during the pandemic. The thesis revealed that during a crisis, both positive and negative reactions can co-occur. The pandemic created a state of exception within the penal system; therefore, an increase in rights violations occurred. However, an opportunity for positive change also emerged. Advocates used this opportunity to change their strategies and maintain their advocacy. By contrast, the government did not seize the same opportunity, as is evidenced by how the recommendations that advocates had been supporting were not implemented properly to protect prisoners.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".