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Record W2968129771 · doi:10.1162/glep_a_00516

Finding Common Ground: Negotiating Downstream Rights to Harvest with Upstream Responsibilities to Protect—Dairies, Berries, and Shellfish in the Salish Sea

2019· article· en· W2968129771 on OpenAlexaff
Emma S. Norman

Bibliographic record

VenueGlobal Environmental Politics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsGeneral partnershipIndigenousCorporate governanceNegotiationDownstream (manufacturing)Upstream (networking)Common groundEnvironmental planningShellfishBayBusinessEnvironmental resource managementFisheryPolitical scienceSociologyGeographyLawEcologyEngineeringEnvironmental scienceBiologyCivil engineeringTelecommunicationsFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Harvesting shellfish is an important cultural and economic activity for coastal Indigenous communities throughout the Salish Sea. However, for the Lhaq’temish People of Lummi Nation, upstream agricultural pollution has rendered this inherent right impossible for almost two decades. In an attempt to reopen the shellfish beds, Lummi Nation leaders developed the Portage Bay Partnership, which aims to address the upstream pollution problem through relationship building and shared connection to place. The partnership brings to light several key points: (1) efforts to integrate different community views of place to develop a relational approach to shared water governance, (2) the use of legal tools to incentivize relationship building, and (3) the continued challenges associated with competing governance frameworks and worldviews. This partnership opposes a system that has been set up to systemically exclude or disenfranchise Indigenous communities, replacing a governance model based on acquired rights with one that prioritizes relationships and responsibilities.

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)0.007
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.019
Scholarly communication0.0060.006
Open science0.0010.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.235
Teacher spread0.229 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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