Migrant farmworkers: Resisting and organising before, during and after COVID‐19
Bibliographic record
Abstract
Abstract Migrant farmworkers are a ubiquitous but invisibilised, expropriated and exploited component of the global agricultural economy. Their conditions took centre‐stage during the COVID‐19 pandemic. Fear of production disruption in the migrant labour‐intensive sectors led to foreign workers being deemed ‘essential’ in many countries, and exceptional procedures and regulations were instituted that further increased their exploitation, illnesses and deaths. However, the pandemic has not merely exposed the long‐established structures of racialised exploitation and expropriation in the domain of farm work. Although it exacerbated the precariousness of the living and working conditions defining the reality of migrant farm workers, there is evidence that the pandemic also strengthened farmworkers' individual and collective consciousness, along with forms of organisation and resistance. The symposium ‘Migrant Farmworkers: Resisting and Organizing before, during and after COVID‐19’ explores two dimensions reflected in migrant farmworkers' realities during the pandemic. First, the contributions look at the general conditions defining power structures and material outcomes within the political economy of agriculture before and during the pandemic. Second, they explore the conditions under which resistance and solidarity emerged to question established structures of exploitation.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 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".