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Record W2905934587 · doi:10.1080/02673037.2018.1535055

‘Take whatever you can get’: practicing Housing First in Alberta

2018· article· en· W2905934587 on OpenAlexaffabout
Jalene Anderson-Baron, Damian Collins

Bibliographic record

VenueHousing Studies · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsApartmentEconomic rentRentingAffordable housingRental housingHousing FirstEconomic shortageContext (archaeology)BusinessService (business)Service providerSupportive housingQualitative researchEconomic growthPublic relationsFinanceMarketingPublic economicsEconomicsPolitical scienceSociologyPsychologyGovernment (linguistics)MedicineGerontologyMarket economyLaw

Abstract

fetched live from OpenAlex

Housing First (HF) is an increasingly widespread and influential response to chronic homelessness. Programs using an HF approach typically rely on market apartments to house homeless clients as rapidly as possible. This reliance means HF programs are dependent on the availability and affordability of market housing. Little attention has been given to how shortages of affordable rental housing influence the practice of HF. To address this gap, we undertook qualitative research in Alberta, Canada. Interviews with service providers revealed that high rents and low vacancy rates had profound impacts on program operations, and complicated efforts to follow HF principles. Clients often experienced delays in being housed and felt pressure to accept the first apartment they were offered. In response, HF programs devoted resources to improve relationships with landlords. Ultimately, however, reliance on market housing undermined programs’ ability to fulfil the potential of HF in the Alberta context.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.052
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.008
Scholarly communication0.0030.001
Open science0.0030.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.110
GPT teacher head0.446
Teacher spread0.336 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

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