<i>Niqivut</i> (our food)—dimensions of Inuit country food harvesting and significance in Arctic Canada: bountiful, seasonal, “soul food”
Bibliographic record
Abstract
Harvesting mammals, fishes, birds, eggs, and plants underpins Inuit culture across Inuit Nunangat (Inuit homeland in Canada). Climatic and nonclimatic drivers affect Inuit access to these items and have cascading effects on social determinants of Inuit health. A holistic understanding of the diversity and seasonality of harvesting is needed to support Inuit food sovereignty and security. Our goals were (a) to develop a broader understanding of the diversity and seasonality of harvesting country food across varied geographic contexts in Arctic Canada, and (b) further document the significance and benefits of country food as part of Inuit food sovereignty and systems across a range of communities. Knowledge holders in 14 communities spanning three Inuit regions shared knowledge and perspectives surrounding species harvested seasonally and species’ significance. Results demonstrate the range of overlapping yet diverse country food systems across Inuit regions, the seasonality and temporality of harvesting, and include a “survey” of species related to Inuit culture. This study highlights the cultural significance and benefits (nutritional, medicinal, mental health, connection to culture, and language) of country food; and reinforces that harvesting, processing, sharing, and eating country food are fundamental aspects of Inuit culture, wellbeing, and food sovereignty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".