Weaving together Inuit knowledge and western science: a mixed-methods case study of <i>qilalugaq</i> (beluga whale) in Quaqtaq, Nunavik
Bibliographic record
Abstract
The harvest and consumption of country food is a cornerstone of Inuit culture, sovereignty, food security, and nutrition. Qilalugaq (beluga whales) ( Delphinapterus leucas (Pallas, 1776)) are hunted across the Canadian Arctic and are an especially important food source for Inuit communities in Nunavik, northern Québec, Canada. The presence of environmental contaminants and nutrients in beluga has been the subject of recent research interest, including the role of selenoneine and its interactions with methylmercury. Using interviews conducted in Quaqtaq and analyses of beluga tissue samples harvested by hunters, this study aimed to bridge Inuit knowledge and scientific knowledge to understand how beluga hunting, preparation, and consumption practices may explain the different levels of selenoneine found in Nunavimmiut (Inuit from Nunavik). It also sought to characterize the health, social, and cultural importance of beluga and factors influencing its consumption. Research findings confirmed the important role of beluga in Nunavimmiut culture, food security, and nutrition. Findings documented gender-based consumption practices, including consumption of the selenoneine-rich beluga tail exclusively by women, which may explain previously documented gender differences in blood selenoneine levels. This study demonstrates the utility of weaving Inuit knowledge and scientific knowledge to inform future environmental health research, public health communications, and wildlife comanagement.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".