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Record W249594387 · doi:10.18584/iipj.2015.6.1.2

Missing Pathways to Self-Governance: Aboriginal Health Policy in British Columbia

2015· article· en· W249594387 on OpenAlexafffundvenueabout
Josée G. Lavoie, Annette J. Browne, Colleen Varcoe, Sabrina T. Wong, Alycia Fridkin, Doreen Littlejohn, David Tu

Bibliographic record

VenueInternational Indigenous Policy Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British ColumbiaUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsIndigenousContext (archaeology)Government (linguistics)Corporate governancePolitical scienceHealth policyPublic administrationPublic policySociologyGeographyHealth careLawEcologyManagement

Abstract

fetched live from OpenAlex

This article explores how current policy shifts in British Columbia, Canada highlight an important gap in Canadian self-government discussions to date. The analysis presented draws on insights gained from a larger study that explored the policy contexts influencing the evolving roles of two long-standing urban Aboriginal health centres in British Columbia. We apply a policy framework to analyze current discussions occurring in British Columbia and contrast these with Ontario, Canada and the New Zealand Māori health policy context. Our findings show that New Zealand and Ontario have mechanisms to engage both nation- or tribal-based and urban Indigenous communities in self-government discussions. These mechanisms contrast with the policies influencing discussions in the British Columbian context. We discuss policy implications relevant to other Indigenous policy contexts, jurisdictions, and groups.

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)0.004
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0270.010
Scholarly communication0.0090.002
Open science0.0020.005
Research integrity0.0030.004
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 head0.023
GPT teacher head0.373
Teacher spread0.349 · 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

Citations20
Published2015
Admission routes4
Has abstractyes

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