An Iterative and Participatory Method for Mapping Inuit Knowledge of the Ice and Ocean in Nunatsiavut
Bibliographic record
Abstract
In 2019 and 2021, we held participatory mapping workshops in Nain, Hopedale, Postville, Makkovik, and Rigolet (Nunatsiavut, Canada) to document Labrador Inuit’s knowledge of the sea ice and ocean environment. We provide an overview of the iterative and adaptable methodological approach we used to support collaborative and transcultural marine research, emphasizing participants’ experiences during the process. The participatory mapping method created a space that encouraged participants to recall journeys across different times and places. Sharing these journeys provided essential contextual details connecting social and cultural values to the marine environment, while also conveying information about ice and ocean conditions. This approach resulted in collecting spatial and qualitative narrative data related to the marine environment that reflected local climate patterns and snapshots of unusual events or conditions observed at specific times and locations. We highlight that maps mainly facilitate knowledge-sharing rather than generating knowledge itself. This is evident in how Inuit participants interacted with the maps as objects that evoked memories and prompted movement across land-, sea-, and ice-scapes.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".