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Record W3211601514 · doi:10.1111/faam.12309

First Nations gatekeepers as a common pool health care institution: Evidence from Canada

2021· article· en· W3211601514 on OpenAlexaffabout
Akolisa Ufodike, Oliver Nnamdi Okafor, Michael Opara

Bibliographic record

VenueFinancial Accountability and Management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsGatekeepingHealth carePublic relationsCorporate governanceContext (archaeology)Public administrationGovernment (linguistics)InstitutionPolitical scienceBusinessLawGeography

Abstract

fetched live from OpenAlex

Abstract This study investigates the roles of informal gatekeepers in the context of First Nations health care in Canada. While existing literature focuses on gatekeeping by professionals, such as corporate board members, auditors, general practitioners, and specialists, we present empirical evidence on the role of informal First Nations’ gatekeepers in a health care system. Gatekeepers engage with government and First Nations along a continuum of gatekeeping functions. Drawing on common pool theory, we use a case study to understand the gatekeepers’ roles. We identify First Nations gatekeepers’ roles to include health care program control, resource control, and ecosystem control. We also find that these gatekeepers constitute a viable long‐enduring common pool resource institution that can be useful for the federal government's phased approach in transitioning health care governance to the First Nations.

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)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0100.006
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.273
Teacher spread0.235 · 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 designObservational
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

Citations7
Published2021
Admission routes2
Has abstractyes

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