Modelling optimal diets for quality and cost: examples from Inuit and First Nations communities in Canada
Bibliographic record
Abstract
This review summarizes aspects of the 2017 Canadian Nutrition Society symposium, "Modelling diets for quality and cost: examples from Inuit and First Nations in Canada". Indigenous peoples in Canada experience a high prevalence of nutrition-related chronic disease because of the poor quality and high cost of their food supply. Since European colonization, they have transitioned from a diet of minimally processed traditional foods (game, fish, and plants) procured using pursuits such as hunting, fishing, gathering, and horticulture to a diet comprised mostly of processed market foods. This nutrition transition is the result of factors such as colonial policies and practices; climate change; environmental degradation; contaminants in traditional foods; and limited availability of, or access to, economical and healthful market foods. Presenters Malek Batal and Laurie Chan characterized the contemporary diets of First Nations and Inuit populations and demonstrated novel methods for modelling more optimal diets using 2 datasets: the First Nations Food, Nutrition, and Environment Study and the Inuit Health Survey. It was demonstrated how the NOVA classification characterized the portion of the diet consisting of processed foods. Dietary components were then manipulated to reduce ultra-processed food and drink intake to increase the Healthy Eating Index score. Linear programming was explained as a way to mathematically design theoretical diets that aim to optimize food cost, nutrition quality, and contaminant level of traditional foods. While diet-modelling methodologies have limitations, they provide a basis for engaging Indigenous peoples and governments to develop nutrition goals and policies anchored in contemporary food realities.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".