Migrant Agricultural Workers in Local and Global Contexts: Toward a Better Life?
Bibliographic record
Abstract
Four books published between 2013 and 2014 make a vital contribution towards understanding the political and ideological tools by which states and employers construct hyper‐exploitable agricultural workers. In this review essay, we provide an assessment of how these books have advanced our understandings of migrant farm labour regimes in local and international perspectives. After presenting a synopsis of each text, we critically reflect on key lessons learned, offer questions that merit further attention, and suggest directions for future research. Our review finds that despite wide differences in geopolitical and legal contexts in which migrant agricultural workers cross borders, live and work, there are remarkable resemblances in the ways in which states use (and abuse) migrant labour. Likewise, there are glaring similarities in the consequent vulnerabilities migrants experience. While each author provides compelling and empirically rich observations based on local fields of study, generally lacking are broader global connections and policy discussions about how the problems raised can be meaningfully addressed. Given the seeming ubiquity of exploitative migrant agricultural worker regimes, the fundamental question left largely unanswered is: Must ‘local’ agricultural systems depend on vulnerable imported workers in order to provide affordable food for consumers, or are there workable alternatives to this arrangement?
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".