Why and How to Document the Traditional Food System in your Community: Report from Breakout Discussions at the 2017 Native American Nutrition Conference
Bibliographic record
Abstract
Two conference breakout sessions at the 2017 Second Annual Conference on Native American Nutrition focused on the reasons and methods to document traditional food systems. The sessions included examples from 4 communities of Indigenous Peoples. A total of 60 participants discussed their thoughts and experiences within their communities on documenting traditional food systems. Some of the reasons, or “whys” for the documentation, included reinvigorating the culture to benefit the youth and those who had moved away from the community, preserving Elder knowledge, and increasing the ability to use the local plants. The methods, or “hows” of the documentation discussed included making sure the communities lead projects, protections are in place for the knowledge holders, and creating a contemporary feel for youth. Meeting transportation needs was paramount, as was creating a network of people and communities involved in documenting and reintroducing traditional food systems. This was exemplified by the diverse and experienced participants of these sessions and the associated conference.
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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 | 0.042 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".