MétaCan
Menu
Back to cohort
Record W4362519168 · doi:10.26522/ssj.v17i2.4028

Unpacking the Prison Food Paradox: Formerly Incarcerated Individuals’ Experience of Food within Federal Prisons in Canada

2023· article· en· W4362519168 on OpenAlexaffvenueabout
Amanda Wilson

Bibliographic record

VenueStudies in Social Justice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsMass incarcerationPrisonLegitimacyFood systemsSociologyPunishment (psychology)Political scienceCriminologyPublic relationsFood securityPsychologySocial psychologyLawGeography

Abstract

fetched live from OpenAlex

This paper presents findings from a survey conducted with formerly incarcerated individuals on their experiences of food and food systems within federal prisons in Canada. Beyond affirming the many problems with the quality and quantity of food provided to incarcerated individuals, the findings discussed in this article highlight the multi-faceted and paradoxical role of food behind bars. Food was a tool of punishment and a site of conflict, yet it simultaneously provides an important source of community and camaraderie. While there can be no “just” carceral food system because carceral systems are inherently unjust systems, a conversation about food provisioning within prison helps bring into focus opportunities to improve the material conditions of incarcerated individuals in the short-term as well as openings to question the logic and legitimacy of carceral institutions more broadly. As we are all bound-up in carceral food systems, there is a collective responsibility to interrogate and make visible the realities of carceral food systems in order to work towards non-carceral futures.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.091
GPT teacher head0.377
Teacher spread0.286 · 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 designQualitative
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

Citations5
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueStudies in Social JusticeSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207