Bibliographic record
Abstract
In the past, First Nations and Inuit obtained all of their food from the land and water around them. Traditional food was central to the culture and the way of life. As long as there was enough to eat, traditional food gave everyone all the nutrition they needed to stay strong and healthy. Most people now eat a mix of traditional and store-bought food. For some First Nations and Inuit groups, this shift to more commercial foods has happened very quickly. Reasons for this change in dietary patterns include, but are not limited to, relocation into settlements, decreased access to land, less time and energy and fewer skills for harvesting due to employment, depletion of game, concern for environmental contaminants, and costs of or restrictions on hunting. At the same time, the geographic isolation of many First Nations and Inuit communities is such that nutritious store-bought foods, especially perishable items, are expensive and sometimes difficult or impossible to obtain. The Government of Canada, through the Food Mail program, subsidizes the cost of transporting nutritious foods to remote, isolated communities, but even with such a subsidy, market foods are often much more expensive than they would be in southern urban centres. In some communities, virtually
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 | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.007 |
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".