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Record W2561207071 · doi:10.1111/joac.12199

Migrant Agricultural Workers in Local and Global Contexts: Toward a Better Life?

2016· article· en· W2561207071 on OpenAlexaff
Janet McLaughlin, Anelyse M. Weiler

Bibliographic record

VenueJournal of Agrarian Change · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of TorontoWilfrid Laurier University
Fundersnot available
KeywordsGeopoliticsAgricultureConstruct (python library)PoliticsIdeologyMigrant workersSociologyWork (physics)Political scienceOrder (exchange)Political economyEconomic growthBusinessEconomicsLawGeography

Abstract

fetched live from OpenAlex

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?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.278
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2016
Admission routes1
Has abstractyes

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