“Everyone wears mitts”: reflections on the use of metaphors in knowledge co-production in Nunavut, Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.069 | 0.064 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".