The Flows of Racial Capitalism: Charting the Spread of COVID-19 through Alberta’s Meatpacking Industry
Bibliographic record
Abstract
This article will present a case study of Cargill’s High River meatpacking plant operations to show how at crucial historical junctures racial capitalism shaped its working conditions and in so doing determined the spread of COVID-19. First, the Canadian meatpacking industry’s 1980s-era economic restructuring relocated and reorganized its workforce from a core to peripheral one, allowing for the low wage employment of many precarious workers; this restructuring enabled the Cargill company to gain overwhelming control of the meatpacking industry in Canada and to become a “choke point” in the supply chain. Second, Canadian immigration policy from 2006 to 2010 supported a marked increase in migrant workers to meet the labour market needs of business; this reconstituted the labour class to heighten their disposability. With these pieces in place, the Albertan provincial government could classify meatpackers as “essential workers” who worked even in the face of mass COVID infection in April through June 2020. Across this crucial historical period racial capitalism enabled the plant to circumvent public health interventions protecting workers through the onset of the pandemic. Political championing of business interests, enacted through legislative mechanisms, allowed for the exploitation of workers and consistently rendered workers personally responsible for their own health and safety, despite their lack of control over what exposed them to risk.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".