Race, Food, and Borders: Situating Migrant Struggle in the Okanagan Valley, British Columbia
Bibliographic record
Abstract
Over the past century, the Okanagan Valley's social, economic, and physical landscape has been largely shaped by the region's agricultural industry. Within this landscape migrant farmworkers have an essential role, yet are rendered invisible and remain marginalized. This commentary explores migrants' struggle by looking at the intersections of colonialism, race, borders, and the local food economy. We begin with a historical examination of the racialized nature of the region's agricultural labor force, and also provide an overview of the local food economy. Following this, we outline Canada's Seasonal Agricultural Worker Program (SAWP) and discuss how the SAWP places migrant laborers in positions of precarity, often resulting in worker isolation and superexploitation. We then turn to the social conditions migrant workers encounter upon arriving in the Okanagan Valley by describing the institutional discrimination they face, as well as the everyday prejudices and aggressions they endure due to their status of being labeled both "foreign" and "temporary." Next we provide a brief explanation of settler colonialism, the imposition of borders, and the common struggles shared by migrant workers and Aboriginal people. Finally, we offer some recommendations for change that would ameliorate some of the challenges migrant workers experience upon arriving in the Okanagan.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".