Assessing the contribution of traditional foods to food security for the Wapekeka First Nation of Canada
Bibliographic record
Abstract
The food security crisis and disproportionately high burden of dietary related disease amongst northern Indigenous populations in Canada continues to be a troubling reality with little sign of improvement. The Government of Canada is responding by developing programs to support local food initiatives for northern isolated communities. While such investments appear commendable, the impact of local food harvesting to improve food security has yet to be determined. While there are clear nutritional and cultural benefits to traditional food sources, communities face considerable barriers acquiring it in sufficient amounts because of historically imposed lifestyle changes that have increased food insecurity rates. This study responds by providing a novel multidisciplinary approach that draws from firsthand experiences working with First Nations community members in a remote subarctic region in northwestern, Ontario, to estimate their community’s total food requirement and the amount of wild animal food sources needed to sustain yearly food intake. This transferrable energy demand approach will be critical for policy makers to put into perspective the amount of wild food needed to have an impact on food security rates and ultimately improve dietary related diseases. Novelty: Provide government policy makers information about current harvest yields in a remote northern First Nation to understand the potential contribution of traditional food to improve local food security. Provide Indigenous communities a means to assess local food resources to measure the caloric contributions of traditional foods toward household food security.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".