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Record W2293172535 · doi:10.1093/bjc/azv050

‘A Precarious Place’: Housing and Clients of Specialized Courts

2015· article· en· W2293172535 on OpenAlexaffabout
Marianne Quirouette, Kelly Hannah‐Moffat, Paula Maurutto

Bibliographic record

VenueThe British Journal of Criminology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSociologyLibrary scienceMedia studiesLawCriminologyArt historyPolitical scienceHistoryComputer science

Abstract

fetched live from OpenAlex

Specialized courts rely on partnerships with community agencies to address multiple issues related to offending. Despite their popularity, little is known about the implications of such partnerships or about how stakeholders negotiate client support, therapeutic interventions and correctional practices. We analysed six Canadian sites (four drug treatment courts and two community/wellness courts), specifically focusing on how they conceptualize and respond to housing issues. We found that practices are pushing the boundaries of punishment and producing unintended consequences related to (1) positioning homelessness as criminogenic, (2) emphasizing short-term stability to the detriment of longer-term solutions and (3) facilitating enhanced supervision/knowledge exchange. When legal concerns dominate, therapeutic potential is compromised, along with efforts to restructure supports needed by marginalized offenders in the community.

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)0.002
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0180.012
Scholarly communication0.0050.003
Open science0.0030.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.162
GPT teacher head0.414
Teacher spread0.252 · 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

Citations26
Published2015
Admission routes2
Has abstractyes

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