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Record W4214612133 · doi:10.18192/jpp.v30i1.6226

CSC’s COVID-19 Response: Inside Canada’s First Federal Penitentiary Outbreak at Mission Medium

2022· article· en· W4214612133 on OpenAlexvenueaboutno aff
Dean W. Roberts

Bibliographic record

VenueJournal of Prisoners on Prisons · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsOutbreakCoronavirus disease 2019 (COVID-19)Political scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyBiologyMedicine

Abstract

fetched live from OpenAlex

The following is an account of the fi rst major COVID-19 outbreak in a Canadian prison that took place at Mission Medium Institution located in the community of Mission, British Columbia, Canada in April 2020 and its impacts on institutional life that still reverberate today.During the outbreak at this federal penitentiary operated by Correctional Service Canada (CSC), a total of 120 federally sentenced men were infected with the coronavirus (CSC, 2021), one of which died (CSC, 2020).In chronicling the measures taken in a stated eff ort to prevent and manage the spread of COVID-19, including lockdowns and other restrictive measures, I argue that CSC's public health response to the pandemic has subjected its prisoners to torturous conditions of confi nement that are psychologically harmful, which will ultimately undermine public safety down the road.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.122
GPT teacher head0.421
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

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