Surviving the Pandemic on the Inside: From Crisis Governance to Caring Communities
Bibliographic record
Abstract
The COVID-19 global pandemic spurred unprecedented global lock downs and quarantines. In looking at the response to and the impacts of COVID-19 in Canadian prisons, we show how the global pandemic can illuminate the impacts of imprisonment to make them more tangible and relatable to the wider public who are largely disconnected from the prison experience. We begin this article by conceptualizing how ‘crisis governance’ produces new practices of penal operations that become problematically normalized, even after the crisis fades. This is reflected in the Correctional Service of Canada’s (CSC) “new normal” document, a strategic plan and management protocol introduced by federal corrections in response to the pandemic. To highlight the new penal regime, we focus our analytical efforts on the mental health impacts of the CSC’s COVID-19 new governance and response plan as they have been reported by way of lived experiences of federal incarceration in Canada throughout the pandemic. We argue that in their efforts to securitize the environment in light of the very real health risks that COVID-19 presents, the actions taken and not taken by prison officials and Canadian politicians primarily left prisoners isolated, disconnected, and without supportive resources, which aggravates underlying mental health conditions and creates additional emotional distress for vulnerable people. Not only can this approach detrimentally impact staff-prisoner relations, it also fails to consider the value of decarceration as an essential and possibly life-saving component of the correctional COVID-19 risk management response plan. We conclude by considering more humane recommendations that would instead prioritize the creation of “caring communities” where collectives of people support each other’s health and well-being, over punitive and austere management practices. Given that the detrimental effects of isolation are now also being felt to a certain extent by those who are not incarcerated, this penal move to a “new normal” should signal to the wider public the ongoing and exceptionally damaging implications of imprisonment.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.055 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".