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Record W4406854151 · doi:10.1139/as-2024-0038

“Everyone wears mitts”: reflections on the use of metaphors in knowledge co-production in Nunavut, Canada

2025· article· en· W4406854151 on OpenAlexafffundvenueabout
Nicolas D. Brunet, J. E. Milton, Sarah-Anne Thompson, Michael Milton, Dominique Henri, Shelly Elverum

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Guelph
FundersCanada Research Coordinating CommitteePolar Knowledge Canada
KeywordsProduction (economics)Knowledge productionSociologyKnowledge managementComputer scienceEconomics

Abstract

fetched live from OpenAlex

Co-production has emerged as foundational to meaningful community-based Arctic research, placing Inuit leadership as central to knowledge creation. When founded upon strong relationships, co-production incorporates Indigenisation into research design, theoretical basis, data collection, and analytical strategies. Here, we discuss and reflect on our experiences using metaphor as a bridge to meaningful engagement and knowledge co-production between southern-based researchers and an Inuit youth team. As a guiding metaphor for this project, we used the process of “making mitts” ( pualungnit in Inuktitut; pualuk meaning “mitten”), from identifying a need, to the action of crafting and using them. Our work indicates that metaphors can be very useful in facilitating knowledge co-production in a cross-cultural context by being adaptive to youth needs and overlaying the familiar and the unfamiliar. The introduction of the “mitt” metaphor led to a completely novel creation process where youth contributors felt empowered, even compelled, to critique the work undertaken, freely and honestly without fear of repercussion or embarrassment. In discussing the process of sewing mitts, they stepped into the role of experts. As a result, the metaphor facilitated the creation of accessible and safe spaces within which to work and build relationships.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.650
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.082
GPT teacher head0.361
Teacher spread0.279 · 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.

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

Citations0
Published2025
Admission routes4
Has abstractyes

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