Exploring settler-Indigenous engagement in food systems governance
Bibliographic record
Abstract
Abstract Within food systems governance spaces, civil society organizations (CSOs) play important roles in addressing power structures and shaping decisions. In Canada, CSO food systems actors increasingly understand the importance of building relationships among settler and Indigenous peoples in their work. Efforts to make food systems more sustainable and just necessarily mean confronting the realities that most of what is known as Canada is unceded Indigenous territory, stolen land, land acquired through coercive means, and/or land bound by treaty between specific Indigenous groups and the Crown. CSOs that aim to build more equitable food systems must thus engage with the ongoing impacts of settler colonialism, learn/unlearn colonial histories, and build meaningful relationships with Indigenous peoples. This paper explores how settler-led CSOs engage with Indigenous communities and organizations in their food systems governance work. The research draws on 71 semi-structured interviews with CSO leaders engaged in food systems work from across Canada. Our analysis presents an illustrative snapshot of the complex and ongoing processes of settler-Indigenous engagement, where many settler-led CSOs aim to work more closely with Indigenous communities and organizations. However, participants also recognize that most existing engagements remain insufficient. We share CSOs’ practices, tensions, and lessons learned as reflections for scholars and practitioners interested in the continuous journey of building settler-Indigenous partnerships and reimagining more just and sustainable food systems, work which requires iterative and critically reflexive learning.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.031 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".