Using food systems to foster Indigenous youth leadership in global health
Bibliographic record
Abstract
First paragraph: In October 2023, I was very fortunate to have been invited to attend a Global Indigenous Youth Forum where Indigenous youth from all over the world gathered. At the forum, there was a large focus on environmentalism, Indigenous youth advocacy, and protecting cultural diets and Indigenous food systems through combining traditional and Western science. Cultural diets are the traditional diets of Indigenous peoples, and are “derived from the land” (Native Women’s Association of Canada, 2012, p. 10). These were all incredibly important discussions, and it was inspirational to see Indigenous youth from all over the world advocate for one another despite speaking different languages and having different backgrounds. But I also noticed that the connection between Indigenous food systems and health was glossed over. As a Cree-Saulteaux Indigenous youth, I grew up hearing that food is an important aspect of health, and the dietary decisions we make affect our physical, mental, emotional and spiritual wellness. Especially given that many diseases within Indigenous communities, including my own, are diet-related, I was inspired to write this commentary to advocate for the link between Indigenous food systems and health, and to advocate for global Indigenous youth leadership within global health. . . .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".