Indigenizing Food System Planning for Food System Resiliency
Bibliographic record
Abstract
Problem, research strategy and findings Planners conduct community food assessments for the purpose of supporting community food security efforts. However, assessments of community food assets, including their availability and access, are often limited in their consideration of ecological and cultural assets that are central to Indigenous food systems. Moreover, what are considered mainstream food assets may not reflect the everyday lived experiences of Indigenous peoples and traditional food sources. In this study we applied a citizen science–led photovoice food assessment, involving six Indigenous participants from Kitselas (Ts’msyen) First Nation in Canada. Using practice theory, the findings show how Indigenous concepts of relationality and reciprocity are intertwined in land-based food-related practices, which highlights the need for a holistic approach in documenting and planning around local food assets.Takeaway for practice The field of planning needs to respect and support Indigenous food sovereignty in planning policies. We recommend a more inclusive approach to community food assessment in planning, understanding how cultural food assets matter, and increasing community support to revitalize Indigenous food systems in culturally relevant ways.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".