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Record W4386221663 · doi:10.1111/hojo.12538

Youth carceral deinstitutionalisation and transinstitutionalisation in Ontario: Recent developments and questions

2023· article· en· W4386221663 on OpenAlexaffabout
Linda Mussell, Jessica Evans

Bibliographic record

VenueThe Howard Journal of Crime and Justice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSituatedImmigration detentionContext (archaeology)CriminologyCriminal justiceEconomic JusticeSociologyPolitical scienceRestorative justiceLawHuman rightsGeography

Abstract

fetched live from OpenAlex

Abstract In early 2021, half of the youth detention centres in Ontario, Canada, were abruptly closed. We ask how this development can be understood in relation to broader explanations of youth detention closures in Canada, which cite the success of the Youth Criminal Justice Act (YCJA) and the best interests of youth, and the broader international context. Using a process tracing methodology to analyse existing data, we demonstrate that these closures had less to do with the interests of youth, and were primarily a cost‐effective calculation. We demonstrate this by pointing to three key developments: (i) the transference of institutionalised carceral logics onto community service providers; (ii) an undermining of the principle of ‘relationship custody’; and (iii) a focus on high‐capacity and high‐security detention centres, over smaller, locally situated open detention centres.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.944

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.008
Science and technology studies0.0080.011
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0010.002
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 head0.077
GPT teacher head0.322
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2023
Admission routes2
Has abstractyes

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