Exploring activist perspectives on Indigenous-settler solidarity in Toronto’s food sovereignty movement
Bibliographic record
Abstract
While food movements have increasingly taken up the framework of Indigenous food sovereignty in their work, settler food activists continue to define food systems on stolen lands. In this article, we explore whether and how food activists in Toronto are building solidarity with Indigenous peoples and movements in their work. Drawing on semi-structured interviews with food activists and content analysis of Toronto food organizations, we identify three main themes: (un)learning, relationship-building, and systemic constraints and visions for the future. Our findings reveal that many settler food activists are engaging in (un)learning processes, building decolonizing relationships, and supporting greater Indigenous leadership at their organizations. However, participants’ solidarity-building efforts are in the minority among food organizations more broadly, and there is significant work to be done to prioritize Indigenous struggles for land and sovereignty in food movement work. Further, NGO structure and function, corporatized and donor-centric funding models, and settler colonialism more broadly, significantly constrain the capacities of food organizations to align with Indigenous goals and visions. We argue that settler food activists have a responsibility to more deeply consider the role of food activism in upholding and challenging settler colonialism, to let go of settler claims to authority over food and knowledge systems on stolen lands, and to advocate for deeper systemic changes that redistribute power and resources to Indigenous peoples and Indigenous-led initiatives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.023 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".