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Record W2409014925 · doi:10.1111/imig.12266

Troubling the Fields: Choice, Consent, and Coercion of Canada's Seasonal Agricultural Workers

2016· article· en· W2409014925 on OpenAlexaffabout
Stephanie J. Silverman, Amrita Hari

Bibliographic record

VenueInternational Migration · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsCoercion (linguistics)Agency (philosophy)Argument (complex analysis)State (computer science)DocumentationInjusticePolitical scienceFarm workersSociologyAgricultureEconomic growthLawCriminologyEconomicsSocial scienceGeography

Abstract

fetched live from OpenAlex

Abstract This article brings a new, theoretically minded approach to weighing the relative utilities and harms of Canada's Seasonal Agricultural Worker Program ( SAWP ) without dismissing the agency of SAWP enrollees or arriving at an abolitionist argument to end Temporary Migrant Worker ( TMW ) programmes in Canada. Building on the anti‐trafficking debate within feminist migration studies, we evaluate the availability and exercise of consent, choice, and coercion among SAWP workers. We draw on extensive documentation by scholars across disciplines to contextualize the SAWP within a socio‐economic history that engendered and continues to legitimize the “success” of the programme in both Mexico (the largest sending state) and Ontario (the largest provincial recipient of workers). Our analysis suggests that, while grievous, the SAWP 's structural injustice ought not to preclude individuals from migrating and earning wages. The article concludes with recommendations to create a fairer avenue for Mexican workers into, through, and out of the SAWP .

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)0.000
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 score0.530
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.279
Teacher spread0.263 · 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 routes2
Has abstractyes

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