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Record W4402783452 · doi:10.1080/07409710.2024.2405798

Slow violence and the coloniality of carceral foodways in Canadian federal prisons

2024· article· en· W4402783452 on OpenAlexaffabout
Sophie Lachapelle, Jennifer M. Kilty

Bibliographic record

VenueFood and Foodways · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFoodwaysCriminologyPolitical scienceDeportationSociologyImmigrationLawAnthropology

Abstract

fetched live from OpenAlex

The disproportionate incarceration of Indigenous peoples continues to be a disturbing trend in Canada, prompting calls to problematize prison as a modern manifestation of settler-colonialism. To address this call, we explore the disconcerting similarities between historical accounts of food insecurity endured by Indigenous children in residential schools and our participants’ shocking food experiences in Canadian federal prisons. Using qualitative data from 57 semi-structured interviews with formerly incarcerated people, we compare participants’ experiences of poor-quality food, chronic hunger, and malnutrition to those of students in residential schools, problematizing the inadequate food provision in Canadian prisons in the present despite government assurances that prison food meets national nutritional guidelines. Ultimately, we contend that settler-colonialism continues to be weaponized against Indigenous people in prison – and incarcerated people, generally – through the slow violence of carceral food provision, perpetuating inequitable health outcomes for communities historically pushed to the margins of Canadian society.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.020
GPT teacher head0.292
Teacher spread0.272 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2024
Admission routes2
Has abstractyes

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