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Record W4311268049 · doi:10.1139/as-2022-0025

Indigenous-led conservation in the Arctic supports global conservation practices

2022· article· en· W4311268049 on OpenAlexaffvenue
Victoria Buschman, Enooyaq Sudlovenick

Bibliographic record

VenueArctic Science · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIndigenousCircumpolar starTraditional knowledgeToolboxArcticPolitical scienceBest practiceEnvironmental resource managementEnvironmental planningBiodiversity conservationGeographyBiodiversityEcologyEngineeringEconomics

Abstract

fetched live from OpenAlex

Amid growing recognition for the role of global conservation initiatives in protecting biodiversity and mitigating climate change impacts, the interest in Indigenous-led and Indigenous-centered conservation in the circumpolar Arctic is also on the rise. Through literature and practice, Indigenous communities in the Arctic are shaping the global discourse around conservation approaches, mechanisms, and strategies, and are challenging colonial conceptions of how lands, waters, and species should be used, managed, and protected. Indigenous approaches, mechanisms, and strategies often differ from those found in the global conservation toolbox and rather focus on local priorities, Indigenous knowledge, traditional practices, sovereignty, and self-determination. Direction on how conservation should evolve and overcome challenges and related burdens is best given by Indigenous communities, scholars, organizations, and governments. Valuing Indigenous knowledge and supporting community-level initiatives, strategies, and practices comes with the benefits of understanding, forwarding, and implementing community priorities, needs, and values through attention and focus on funding, Indigenous-led research and management, and mutual mentorship. In addition to benefiting conservation itself, biodiversity research conducted within Indigenous homelands has the opportunity to serve as a model for how regional, national, and international initiatives best engage with Indigenous knowledge, conservation practice, and policy development in the Arctic and beyond.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0120.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.413
Teacher spread0.350 · 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 teacher head, not a consensus.

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

Citations14
Published2022
Admission routes2
Has abstractyes

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