Insights from the remote co‐creation of an Indigenous knowledge questionnaire about aquatic ecosystems in Kinngait, Nunavut
Bibliographic record
Abstract
Abstract There is growing interest in co‐developing research projects that more fully address the priorities of Indigenous communities throughout the Canadian Arctic and beyond. However, details regarding collaborative methods are often not adequately described in the literature. Here, we describe a process to remotely co‐create a questionnaire compiling Indigenous knowledge about local aquatic species and their habitats with the community of Kinngait, Nunavut. This project was undertaken in response to interest expressed by the Aiviq Hunters and Trappers Association in understanding and assessing the impacts of climate change on coastal ecosystems. Researchers from Fisheries and Oceans Canada and academic partners drafted an initial questionnaire that was revised through a series of collaborative sessions with community‐based technicians. We detail the stages of this process and discuss elements that enabled co‐creation including: adaptable and frequent communication, community technician roles, and a pre‐existing partnership. This paper emphasizes that project co‐development and the co‐creation of research tools can be a mutually beneficial process that can broaden our collective understanding of the impacts of climate change on Arctic aquatic ecosystems.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".