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Record W4408013770 · doi:10.1139/as-2024-0015

Community knowledge exchange in research leads to innovation and action in the Arctic

2025· article· en· W4408013770 on OpenAlexaffvenueabout
Shari Fox, Henry P. Huntington, Florian Stammler, Bruce C. Forbes, Lene Kielsen Holm, Vladimir Alexeev, Ekaterina Alexeeva, Charlene Apok, Vasily Balanov, Robert Comeau, Bibi Frederiksen, Aytalina Ivanova, Jacob Jaypoody, Angunnguaq Josefsen, Eema Kautuk, Robert Kautuk, Erik Kielsen, Igor Kolesov, Timo Kumpula, Elna Magga, Juha Magga, Nuccio Mazzullo, Vittus Nielsen, George Noongwook, Toku Oshima, Pitseolak Pfiefer, Nikolay Rufov, Taanja Sanila, Nestor Sleptsov, Mirva Tapaninen, Tatiana B. Tikhonova, Karl Tobiassen

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsInuit Tapiriit KanatamiNunavut Research InstituteGovernment of NunavutCarleton University
Fundersnot available
KeywordsLead (geology)Action (physics)ArcticThe arcticPower (physics)BusinessKnowledge managementComputer scienceOceanographyPhysics

Abstract

fetched live from OpenAlex

Indigenous peoples from Arctic communities who are engaged in various aspects of science, research, and community work have much to share with one another. Here, we—a team of Indigenous Arctic community members and visiting researchers—present examples of such sharing during and after four knowledge exchange gatherings that formed the core of a multi-year project, bringing together Indigenous scholars, harvesters, whalers, herders, leaders, and allies from Arctic regions of Canada, Alaska, Greenland, Finland, and Siberia. We present community-to-community knowledge exchange and describe specific examples of exchange approaches, methods, successes, and challenges, as well as direct outcomes from facilitating these interactions. We suggest that the remarkable nature of those outcomes was the result not of something unique to our project, but of the inherent value of creating a venue and opportunities where people felt at ease conversing with others from elsewhere in the Arctic through loosely structured conversations that were situated in place. By not having rigid objectives to follow, participants were able to explore topics that mattered most to them. We argue this approach can lead to un-anticipated results which are best embedded in Indigenous Arctic communities. This paper demonstrates some of such results. The lessons learned through this experience are intended to contribute to conversations on research that are designed to practically respond to community needs.

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)0.018
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 score0.285
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.432
GPT teacher head0.581
Teacher spread0.149 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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