“We call it soul food”: Inuit women and the role of country food in health and well-being in Nunavut
Bibliographic record
Abstract
Indigenous knowledge is central to understanding environment and health sciences in the Arctic, yet limited research in these fields has explored the human–animal–environment interface from the unique perspectives of Inuit women. Using a community-led, Inuit-centred research approach, we characterized the use and meaning of country food in the context of community well-being for Inuit women in Nunavut, Canada. In-depth conversational interviews and focus groups ( n = 16) were held with Inuit women ( n = 10) who are knowledge holders in the Qikiqtani region that hold decades of country food knowledge. Data were analyzed using thematic analysis and constant comparative methods. Inuit women described country food in the context of (1) well-being, connection, and identity, (2) hunger, craving, and healing, (3) food security and nourishment, and (4) change and adaptation. Inuit women described a wide range of country food as central to physical and mental health, food security, identity, culture, healing and medicine. Adaptive strategies were discussed, such as eating more fish when caribou were scarce. This research highlights the critical role of country food for health and well-being for Inuit women and shares knowledge and perspective that is relevant to wildlife and environment researchers, public health practitioners, policy makers, and others interested in advancing health, well-being, and food sovereignty in Inuit communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".