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Record W3123851214 · doi:10.7202/1074199ar

Itinérance, santé mentale, justice

2020· article· fr· W3123851214 on OpenAlexaffvenue
Laurence Roy, Marichelle Leclair, Michelle Côté, Anne G. Crocker

Bibliographic record

VenueCriminologie · 2020
Typearticle
Languagefr
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversité de MontréalInstitut national de psychiatrie légale Philippe-PinelMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Plusieurs programmes et services ont été développés en vue d’adapter les interventions policières et les processus judiciaires aux besoins des personnes ayant des troubles mentaux, de surcroît lorsque celles-ci sont en situation d’itinérance. La présente étude adopte un devis qualitatif descriptif afin d’explorer l’expérience qu’ont les personnes vivant à la fois une situation d’itinérance et un trouble mental de ces services. L’analyse de six entretiens révèle les représentations complexes que se font les participants de leur implication judiciaire, entre sentiment de responsabilité et d’injustice ; le manque de légitimité vécu dans la plupart de leurs interactions, auquel l’accompagnement offre parfois un contrepoids ; et enfin des perceptions distinctes des services selon leur nature « régulière » ou « alternative ». Les participants mettent à l’avant-plan dans leurs récits les principes de la justice procédurale, en particulier ce que des processus dits « alternatifs » permettent à cet égard, mais également le caractère exceptionnel d’interactions respectant ces principes. Les résultats nous amènent à interroger la capacité des diverses institutions sociales à offrir des services vécus comme justes par les personnes situées au confluent d’identités sociales marginalisées, à différents moments de leurs parcours.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.011

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 head0.494
GPT teacher head0.503
Teacher spread0.009 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2020
Admission routes2
Has abstractyes

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