From Palestine to Turtle Island
Bibliographic record
Abstract
This paper examines the historical and ongoing role of food as both a tool of colonization and a means of liberation, focusing on Palestine and Turtle Island (Canada). In Gaza, the latest wave of Israeli military violence, described by the UN as genocidal, uses food militarization and weaponization as key tactics of settler colonial control. These strategies, rooted in colonial and capitalist systems, have long been employed by settler states like Canada to suppress Indigenous populations. The destruction of food systems in Palestine is part of a broader attack on land sovereignty, reflecting similar patterns of colonial land theft and environmental devastation in North America. Gaza now suffers from extreme food insecurity and famine, exacerbated by large-scale environmental destruction. Despite this, food sovereignty remains a crucial aspect of resistance for Palestinians and Indigenous peoples across the world. This paper draws on a panel discussion organized by the Canadian Association for Food Studies/L’Association canadienne des études sur l’alimentation (CAFS/ACÉA), featuring insights from three scholars who connect food systems to colonialism and struggles for self-determination. The discussion underscores the importance of Indigenous movements and mutual aid networks in the fight for land, food, and cultural sovereignty. These localized struggles are part of a larger global resistance against imperialism and colonialism, illustrating the power of food sovereignty as a means of survival and liberation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".