“Where Creator Has My Feet, There I Will Be Responsible”: Place-Making in Urban Environments through Indigenous Food Sovereignty Initiatives
Bibliographic record
Abstract
There is a growing emergence of Indigenous Food Sovereignty (IFS) initiatives across urban centers within many regions of Canada. Urban Indigenous communities are leading these efforts to revitalize Indigenous foods and agricultural practices while promoting food security and increasing Land-based connections within cities. However, the socio-ecological environments within these urban contexts affect IFS initiatives in unique ways which have not been previously explored. This study addresses these gaps by drawing on qualitative interviews with seven urban Indigenous people leading IFS initiatives within Grand River Territory (situated within southern Ontario, Canada). Applying community-based participatory methods, this research explored how place impacts IFS initiatives within urban environments. Thematic analysis generated two overarching thematic categories: Land access, and place-making practices, revealing a bi-directional, dynamic interaction between place and urban IFS initiatives. Relationships with landowners, control of land, and external factors determined how Land was accessed in urban environments. Place-making practices involved fostering relationships with Land, upholding responsibilities, and cultivating Land-based knowledges. Therefore, IFS initiatives are impacted by Land access, but also facilitate place-making for urban Indigenous Peoples. These findings demonstrate pathways towards Indigenous self-determination and IFS within urban contexts, which can be applicable to other urban 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".