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Record W2896816223 · doi:10.1162/glep_a_00477

Renegotiating the Columbia River Treaty: Transboundary Governance and Indigenous Rights

2018· article· en· W2896816223 on OpenAlexaff
Alice Cohen, Emma S. Norman

Bibliographic record

VenueGlobal Environmental Politics · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsTreatyScholarshipIndigenousContext (archaeology)NarrativeEnvironmental governanceIndigenous rightsPolitical scienceCorporate governanceState (computer science)SociologyLawGeographyHuman rightsEcologyBusinessArchaeology

Abstract

fetched live from OpenAlex

This article builds on regional environmental governance (REG) scholarship to explore alternatives to conventional transboundary agreements. Specifically, we use two narratives to tell the story of one river variously known as Wimahl, Nich’i-Wàna, or Swah’netk’qhu, and, more recently, the Columbia River. We suggest that the state-led narrative of the signing and implementation of the 1964 Columbia River Treaty has obscured Indigenous narratives of the river—a trend replicated in most scholarship on transboundary environmental agreements more broadly. In exploring these narratives, we: situate the silencing of Indigeneity in the 1964 Columbia River Treaty; highlight the reproduction and amplification of that silence in the relevant literature in the context of strengthened Indigenous rights; and explore what a multilateral—as opposed to binational—approach to environmental agreements might offer practitioners and scholars of REG.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.681
Threshold uncertainty score0.634

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.042
Scholarly communication0.0070.005
Open science0.0010.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.225
Teacher spread0.218 · 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 designNot applicable
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

Citations21
Published2018
Admission routes1
Has abstractyes

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