Relationality, Responsibility and Reciprocity: Cultivating Indigenous Food Sovereignty within Urban Environments
Bibliographic record
Abstract
There are collective movements of Indigenous food sovereignty (IFS) initiatives taking up place and space within urban environments across the Grand River Territory, within southern Ontario, Canada. Indigenous Peoples living within urban centres are often displaced from their home territories and are seeking opportunities to reconnect with culture and identity through Land and food. This research was guided by Indigenous research methodologies and applied community-based participatory research to highlight experiences from seven Indigenous community members engaged in IFS programming and practice. Thematic analysis revealed four inter-related themes illustrated by a conceptual model: Land-based knowledge and relationships; Land and food-based practices; relational principles; and place. Participants engaged in five Land and food-based practices (seed saving; growing and gathering food; hunting and fishing; processing and preserving food; and sharing and distributing), guided by three relational principles (responsibility, relationality, and reciprocity), framed by the social and physical environments of the place. Key findings revealed that employing self-determined processes to grow, harvest, and share food among the Indigenous community provide pathways towards IFS. This study is the first to explore urban IFS initiatives within this region, offering a novel understanding of how these initiatives are taking shape within urban environments.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".