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Record W2107155426 · doi:10.1177/1473325008089632

Housing for Aboriginal Ex-offenders in the Urban Core

2008· article· en· W2107155426 on OpenAlexaffabout
Jason Brown, Susan Wingert, Nancy Higgitt, Dilly Knol, Heather Block, Murray Barkman, Catherine Charette

Bibliographic record

VenueQualitative Social Work · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsWinnipeg Regional Health AuthorityCentre for Family MedicineUniversity of ManitobaWestern University
Fundersnot available
KeywordsDowntownParticipatory action researchCitizen journalismLived experienceSociologyFace (sociological concept)CriminologyEconomic growthPolitical scienceGeographyPsychologyLawSocial science

Abstract

fetched live from OpenAlex

Aboriginal peoples are over-represented in the Canadian corrections system. When released, they face challenges. Many return to the communities where their friends and family are but have few housing, educational, and employment opportunities, and have difficult choices to make. This participatory research study included interviews with 30 Aboriginal men who had done jail time and had lived in the downtown core of a major Canadian prairie city. It revealed five overlapping themes describing the experience of being released, getting by, finding a community that would receive them, staying out of jail, and trying to get ahead. The results indicate that there is a gap in services for Aboriginal men who have done jail time, and that housing models to meet this need should be developed with the input of residents in local communities.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.440
Threshold uncertainty score0.886

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0140.004
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.307
GPT teacher head0.560
Teacher spread0.253 · 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

Citations6
Published2008
Admission routes2
Has abstractyes

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