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Record W2904509284 · doi:10.1111/gove.12374

Trust, institutions, and indigenous self‐governance: An exploratory study

2018· article· en· W2904509284 on OpenAlexaffabout
William Nikolakis, Harry W. Nelson

Bibliographic record

VenueGovernance · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndigenousInstitutionMainstreamContext (archaeology)PoliticsCorporate governanceInterpersonal communicationSocial trustSociologyPublic relationsPolitical scienceExploratory researchWork (physics)Social scienceLawSocial capitalManagementGeographyEconomics

Abstract

fetched live from OpenAlex

Trust is important to the institutions that make societies successful. Globally, Indigenous peoples are actively building institutions for self‐governance, but there remains little empirical work on trust in this context. To address this gap, we use a mixed methods approach to explore three levels of trust among individual members from three related, but politically distinct First Nations (Indigenous peoples) in British Columbia, Canada. British Columbia offers a unique and dynamic context to explore trust and its relationship with the diverse institutional choices among First Nations. Survey results show that trust is low among respondents and individual variables predictive of trust in mainstream contexts, like education and employment, are not determinative. However, interpersonal trust and political trust were highest in the First Nation most active in institution building, and who linked this with a cultural revitalization narrative. Interviews suggested a bidirectional relationship between individual and collective drivers of trust in this 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)0.004
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 score0.137
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.308
Teacher spread0.278 · 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

Citations39
Published2018
Admission routes2
Has abstractyes

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