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Record W2913564578 · doi:10.1177/1177180118823560

Indigenous nationhood and herring governance: strategies for the reassertion of Indigenous authority and inter-Indigenous solidarity regarding marine resources

2019· article· en· W2913564578 on OpenAlexafffundabout
Suzanne von der Porten, Jeff Corntassel, Devi Dee Mucina

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousCorporate governanceContext (archaeology)HerringColonialismSolidarityPolitical scienceIndigenous rightsGovernment (linguistics)GeographyFisheryLawEcologyEconomicsPoliticsBiologyManagement

Abstract

fetched live from OpenAlex

The modern-day reinvigoration of individual Indigenous nations around the world is connected to broader simultaneous movements of Indigenous nationhood worldwide. The origins, implications, philosophies, and diversities of Indigenous resurgences and resistances continue to be discussed in the growing body of literature on Indigenous governance. This article builds on these discussions by focusing on the applied tools and strategies of Indigenous resurgence. In the context of the Pacific herring fishery in British Columbia, Canada, this research explores the strategies and tools used by three Indigenous coastal nations to apply pressure on the colonial government to abdicate its asserted authority over herring governance. Motivated by a time-honored relationship to herring, we discuss how these Indigenous nations have strategized to try to regain authority over herring governance to protect species and Indigenous access to the fishery. We then discuss this ocean-based resurgence in the context of global Indigenous movements for the reassertion of self-determining authority.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.028
GPT teacher head0.360
Teacher spread0.332 · 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.

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

Citations37
Published2019
Admission routes3
Has abstractyes

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