Sharing Indigenous Foods Through Stories and Recipes
Bibliographic record
Abstract
Participants at the second National Gathering of the Aboriginal Nutrition Network (ANN) were encouraged to submit their favourite traditional recipes. Approximately 40 were received, and a volunteer working group contacted contributors to assist in the creation of a recipe resource with a selection of 12 recipes that included traditional ingredients to promote Indigenous foodways. All contributors were interviewed to share stories about their recipes. Each recipe was then tested, photographed, and developed into a resource handout that was disseminated to a variety of stakeholders. Afterwards, a brief survey was conducted with ANN recipients of the recipes (n = 23) to evaluate the recipe collection. When asked, "Prior to learning about this resource, was a collection of recipes using traditional foods something that you or the communities you work with were interested in?" all respondents answered yes. Nearly all found the recipes easy to follow (91%), and that they were applicable to the interests or needs of the communities they work with (83%). Preserving recipes and building opportunities for dietitians and other health professionals to contribute to traditional food recipe collections facilitates increased knowledge transfer, enhanced cross-cultural understanding, and is generally a useful tool for those working with Indigenous Peoples in Canada.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".