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Record W4403481368 · doi:10.1139/as-2023-0054

Methods and approach to the interdisciplinary and cross-cultural <i>Arctic Alaska Salmon Workshop</i>: critical self-reflections from fisheries scientists

2024· article· en· W4403481368 on OpenAlexvenueno aff
Elizabeth D. Lindley, Peter A. H. Westley

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Ecology, Wildlife Education
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryArcticGeographyThe arcticOceanographyEnvironmental ethicsSociologyGeologyBiologyPhilosophy

Abstract

fetched live from OpenAlex

Redistribution and shifting habitat envelopes are impacting organisms across many taxa, which in turn are impacting Indigenous ways of life. In Arctic Alaska, Pacific salmon are known to have occurred for at least a century, but in recent years appear to be increasingly common. With the goal of holistically understanding and describing these changes in a way that equitably considers Indigenous, local, and western knowledge, we share our experience and methodologies in facilitating the Arctic Alaska Salmon Workshop. We share our perspective, approach, and methods as fisheries natural scientists convening this workshop, which included community-based knowledge holders from the Iñupiat communities of Kotzebue, Point Hope, Utqiaġvik, and Kaktovik, and western scientists and researchers from universities, fishery management agencies, and local community government. After briefly discussing some of the workshop highlights, we conclude with four key takeaways: (1) that the process of co-production of knowledge is an ideal towards, which we must strive, but acknowledge we may rarely, if ever, fully achieve, (2) pursuit of the “good science” should guide our work, (3) examination and assessment of assumptions should occur early and often, and (4) Anglanikina! Make sure you have a good time, (Yup'ik).

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.004
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
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 head0.044
GPT teacher head0.416
Teacher spread0.372 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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