Racialized Migrant Labour in Organic Agriculture in Canada: Blind Spots and Barriers to Justice
Bibliographic record
Abstract
While the organic agriculture movement states a commitment to the principle of ‘fairness’, it is clear that not all organic-certified agriculture offers a more just alternative to dominant modes of growing food. This chapter builds on insights from this previous work by bringing together quantitative insights on migrant labour in organic agriculture in Canada, and analysis of qualitative data using the theoretical tool of racial capitalism. This chapter asks the following questions: What are the patterns of employment of migrant workers on organic farms? And, how does racial capitalism inform our understanding of efforts to integrate the principles of fairness in organic agriculture? I show that organic agriculutre in Canada exemplifies patterns of racial capitalism as the sector has a disproportionately high reliance on migrant workers. From my analysis of qualitative data, I find that individualist narratives are contributing to blind spots and barriers to meaningful collective action that could effectively challenge racial capitalism in the sector and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".