Braiding Food Systems: Co-Constructing Indigenous Seed Systems with Northern Ontario First Nations
Bibliographic record
Abstract
Cold weather and harsh northern climates combined with colonial oppression, racism and marginalization experienced by Indigenous communities in Canada have limited the capacity and presence of food growing in Northern Ontario First Nations. As well, traditional and local food systems consisting of hunting, fishing, gathering, and purchasing food face increasing pressure from climate and land-use change, natural resource exploitation, and rising food and transport costs, threatening food security and sovereignty in northern communities. Responding to calls from First Nations leaders for greater support for food production to complement existing food systems, Braiding Food Systems is a three-year collaborative research project between the University of Guelph, Wiikwemikong Unceded Territory, the Nokiiwin Tribal Council, and the Ontario Ministry of Agriculture Food and Rural Affairs. Working together with four Ontario First Nations communities (Pic Mobert, Fort Williams, Rocky Bay, Wiikwemikong), this project will ‘rematriate’ Indigenous seeds and food growing practices back to communities while facilitating learning through action to build capacity and strengthen Indigenous food sovereignty and food security. This presentation will outline the activities, progress and outcomes from year-one of this research project, describing the relationship building process between community partners, and actions taken to ensure equity and collaboration in research. We will present our workplan and strategy for year 2 and 3, outlining proposed approach to data collection, capacity strengthening, and sustainability. We will present key considerations for year 2 and 3 and share lessons learned to support future collaborative research with Indigenous communities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".