Moving Beyond the Prison Pandemic: Reducing the Use and Harms of Imprisonment, Working Towards Decarceral Futures
Bibliographic record
Abstract
by Olivia Gemma, who is a Research Assistant with the Prison Pandemic Partnership and Dialogue Editor for the Journal of Prisoners on Prisons.The event was organized by the Prison Pandemic Partnership, moderated by Kevin Walby (University of Winnipeg) and Justin Piché (University of Ottawa), and hosted by the University of Ottawa's Human Rights Research and Education Centre.The names of invited speakers are highlighted in bold and italics when they are fi rst introduced to direct the reader's attention to their respective biographical statements.University of Ottawa and Carleton University, and as co-investigator for the Prison Pandemic Partnership.Kevin Walby: March 11 th , 2023 will mark three years since the COVID-19 pandemic was declared.Since the onset of COVID, congregate settings across Canada have been hard hit with infections by those living and working within them.This includes prisons where incarcerated people and staff have been infected at much higher rates than the general population based on the limited data that continues to be publicly disclosed about COVID-19 cases among imprisoned people.These infections have grown year over year, as the pandemic has become normalized, and treated less like a public health emergency.Total reported cases among people in prison, in Canadian federal penitentiaries, nearly doubled from 1,336 cases by the end of February 2021 to 3,489 cases by the end of February 2022, and more than doubled again to 7,716 cases by the end of February this year.During the initial wave of COVID-19, governments enacted several measures like emergency bail releases and expanded temporary absence programs (ETAs) with minimal harm and community benefi ts.This raised the possibility of ongoing diversion and decarceration to reduce the use of imprisonment, especially given that many governments failed to provide reentry support to people exiting incarceration, despite calls from advocates and researchers to do so.These measures have largely now been rolled back, just as the paucity of re-entry support for criminalized people has persisted, undermining both public health and community safety in the process.Throughout the pandemic governments have also introduced a whole lot of repressive measures to deal with COVID-19 in prison, like medical quarantines, isolation regimes often resembling segregation, suspension of programs, suspension of visits, putting in place lockdowns when outbreaks occur or are suspected, and so on.And we've heard from lots of imprisoned people throughout the pandemic that this period has been marked by a lack of personal, protective equipment and cleaning and hygiene supplies, proportionate to the heightened risk posed by COVID-19 in these settings.At the same time, vaccine access and hesitancy among prisoners have emerged as a concern with varying vaccine take-up rates across jurisdictions signalling perhaps that some vaccine rollouts and communication strategies have been more eff ective than others.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".