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Record W4416291627 · doi:10.1038/s44183-025-00158-x

Co-producing ocean plans with Indigenous and traditional knowledge holders

2025· article· en· W4416291627 on OpenAlex
Claudia Baron-Aguilar, Rosabelle Boswell, Andrés M. Cisneros‐Montemayor, Athena E. Copenhaver, Yara Costa, Alejandro Frid, Lisa Hiwasaki, Māui Hudson, Brendan P. Kelly, Jauquelyne Kosgei, Kelsey Leonard, Vera Metcalf, Aphiwe Moshani, Georgina Yaa Oduro, Kenneth Paul, Vatosoa Rakotondrazafy, Gunn‐Britt Retter, Cinda P. Scott, Jacqueline Uku, Mia Strand

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenpj Ocean Sustainability · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsEnvironment and Climate Change CanadaUniversité LavalUniversity of New BrunswickUniversity of VictoriaSimon Fraser University
FundersUniversidad de Los LagosFederation University AustraliaHavforskningsinstituttet
KeywordsTraditional knowledgeIndigenousColonialismGovernment (linguistics)Knowledge-based systemsPower (physics)Best practiceSustainable development

Abstract

fetched live from OpenAlex

Globally, there is a call to recognize and empower Indigenous Peoples and traditional knowledge holders’ leadership in ocean governance, yet the ensuing processes often maintain power inequities and colonial legacies. We propose eight recommendations for equitable, inclusive, and knowledge-based approaches to co-producing sustainable ocean plans with Indigenous and traditional knowledge holders. These are 1) recognizing rights, 2) acknowledging pluralism, 3) aligning policy frameworks, 4) building relationships, 5) prioritizing accessible data, 6) funding Indigenous-led research, 7) addressing intersectionality, and 8) pursuing iterative planning processes.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.228
Teacher spread0.220 · 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