Moving Beyond the Prison Pandemic: Reducing the Use and Harms of Imprisonment, Working Towards Decarceral Futures
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».