‘We have a lot of (un)learning to do’: whiteness and decolonial prefiguration in a food movement organization
Bibliographic record
Abstract
Despite the disproportionate food injustice experienced by Indigenous Peoples, Black people and people of color, food movements have been dominated by white settlers who have had limited success in addressing this injustice. Settler colonialism is increasingly recognized as a root cause of food insecurity for Indigenous Peoples on Turtle Island; it is also a key contributor to food insecurity experienced by Black people and people of color. The racialized exploitation of land and labor central to both settler colonialism and racial capitalism continue to form the backbone of the Canadian food system today, elucidating the important role food movements hold in the struggle for decolonization and racial justice. In this paper we present a case study of the (im)possibilities of white/settlers working towards Indigenous Food Sovereignty and food justice. By analyzing protests linked to Food Secure Canada’s 2018 Assembly, we find that an implicit reliance on representation may have limited the organization’s capacity for change. We propose that unsettling (un)learning, organizational transformation, and participation in broader anticolonial/anticapitalist struggle – what we are calling decolonial prefiguration – offers a more constructive path to decolonized futures that support food sovereignty and justice for all.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".