Land2Lab Project: Reflections on Learning about Mi’kmaw Foodways
Bibliographic record
Abstract
Land2Lab is an evolving community-based intergenerational program that brings together Elders and youth on the land and in the kitchen and lab to share and celebrate Mi’kmaw foodways. Rooted in an Etuaptmumk-Two Eyed Seeing (E-TES) perspective, which acknowledges both Indigenous and Western ways of knowing, the project to date has featured seasonal food workshops, involvement in a children’s summer math camp, a food safety training workshop for teens, and the development of an online toolkit. The project was guided by the Mi’kmaw principle of Netukulimk, which reinforces respect for Mother Earth and stewardship of the land, water, and air for subsequent generations. Involvement of community leaders has been key to successful planning and implementation. While technology plays an important role, lessons learned on the land are critical and will inform efforts to include language and ceremony in future programming. Dietitians are encouraged to support Indigenous-led land-based learning in support of the profession’s commitment to reconciliation.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 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".