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Record W3044408800 · doi:10.1126/science.abd2107

A community-led approach to COVID-19

2020· article· en· W3044408800 on OpenAlexaffabout
Kyle A. Artelle, Kelly L. Brown, Diana E. Chan, Jennifer J. Silver

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsBell (Canada)University of GuelphUniversity of Victoria
Fundersnot available
KeywordsIndigenousFirst nationPolitical scienceGold rushCoronavirus disease 2019 (COVID-19)Spawn (biology)FisheryCorporate governanceSupreme courtFishingGovernment (linguistics)GeographyBusinessLawInfectious disease (medical specialty)BiologyEcology

Abstract

fetched live from OpenAlex

This spring, the Haíɫzaqv (Heiltsuk) First Nation on the West Coast of Canada cancelled their 2020 commercial spawn-on-kelp herring fishery season in response to the coronavirus disease 2019 (COVID-19) pandemic (1). This fishery constitutes a central economic and cultural activity (2), the rights to which the Nation fought to have recognized through precedent-setting efforts, including a successful Supreme Court ruling (3) and the occupation of a federal fisheries office, which led the Canadian government to engage in more meaningful co-management (4). The Haíɫzaqv fishery closure demonstrates the effectiveness of informed, responsible decision-making by community members themselves. Community- and Indigenous-led governance and decision-making authority, as exemplified by the Haíɫzaqv Nation, should be recognized and upheld across the world.

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)0.017
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0260.017
Scholarly communication0.0100.004
Open science0.0040.025
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0210.002

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 head0.220
GPT teacher head0.471
Teacher spread0.252 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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