Learning together: Science and Inuit Qaujimajatuqangit join forces to better understand Iqalukpiit/Arctic Char in the Kitikmeot region
Bibliographic record
Abstract
The Ekaluktutiak Hunters and Trappers Organization documented Inuit Qaujimajatuqangit (IQ) of Arctic Char (iqalukpiit). This project on Arctic Char IQ—traditional knowledge—was part of a collaborative effort with the Ocean Tracking Network and Fisheries and Oceans Canada to study marine migrations of iqalukpiit. Local youth were trained to interview nine elders from the community and document IQ on iqalukpiit. Following the interviews, an Elder-youth camp was held at the traditional fishing site of Iqaluktuuq (the Ekalluk River) in August 2016. The event included community members, fisheries biologists. and social scientists. This community-led project recorded IQ through reports and a video-documentary. It also built local capacity and bridges between generations and disciplines. The Elder-youth camp provided an opportunity for healing on the land and facilitated new insights into iqalukpiit migrations. Together, these outcomes are instrumental in redefining a relationship between people and fish in a changing Arctic. While the goal of the camp was to document iqalukpiit IQ, a more significant goal emerged upon arriving at Iqaluktuuq. People experienced an emotional homecoming that triggered powerful memories of forcibly leaving the land. It also led to a painful realization that subsisting off the land is challenging today given the many competing pressures that people face. Healing together on the land became more important than the iqalukpiit discussion. In spite of this profound context, shared learnings still occurred. For example, a local expert shared a crucial observation on fish migration behaviour and temperature regulation that inspired scientists to analyze their data differently. This ultimately led to new insights into the mechanisms driving the observed behaviour. Such community and agency collaborations, particularly on the land or in the field, may contribute to better environmental understandings. It may also contribute to reconciliation and healing among and between disciplines, generations, and peoples.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".