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Record W2469988673 · doi:10.18584/iipj.2016.7.2.5

Aboriginal Homelessness: A Framework for Best Practice in the Context of Structural Violence

2016· article· en· W2469988673 on OpenAlexafffundvenueabout
Nelly D. Oelke, Wilfreda E. Thurston, D. W. Turner

Bibliographic record

VenueInternational Indigenous Policy Journal · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of CalgaryUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersHuman Resources and Skills Development Canada
KeywordsGeneral partnershipBest practiceIndigenousContext (archaeology)Metropolitan areaCorporate governanceEconomic growthPolitical sciencePublic relationsPublic administrationSociologyBusinessMedicineGeography

Abstract

fetched live from OpenAlex

Homelessness among Indigenous peoples is an important issue in Canada and internationally. Research was conducted in seven metropolitan areas in the four western provinces of Canada to explore current services with the aim of developing a best practices framework to end homelessness for Aboriginal peoples. Sequential mixed methods were used. Key results found agreement that Aboriginal peoples were overrepresented among the homeless and policy determined the approach to and comprehensiveness of services provided. Funding, lack of time, and lack of resources were highlighted as issues. Gaps identified included a lack of partnership, cross-cultural collaboration, cultural safety, and evaluation and research in service provision. Best practices included ensuring cultural safety, fostering partnerships among agencies, implementing Aboriginal governance, ensuring adequate and sustainable funding, equitable employment of Aboriginal staff, incorporating cultural reconnection, and undertaking research and evaluation to guide policy and practices related to homelessness among Aboriginal peoples.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.100
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.481
Teacher spread0.444 · 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 teacher head, 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

Citations15
Published2016
Admission routes4
Has abstractyes

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