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Record W2739136615 · doi:10.1111/imig.12342

Food Security at Whose Expense? A Critique of the Canadian Temporary Farm Labour Migration Regime and Proposals for Change

2017· article· en· W2739136615 on OpenAlexaffabout
Anelyse M. Weiler, Janet McLaughlin, Donald C. Cole

Bibliographic record

VenueInternational Migration · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsWilfrid Laurier UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsFood securityFood sovereigntyFood insecurityIdeologyPremiseAgriculturePolitical scienceEconomic growthSociologyEconomicsDevelopment economicsPolitical economyLawPoliticsGeography

Abstract

fetched live from OpenAlex

Abstract Temporary farm labour migration schemes in Canada have been justified on the premise that they bolster food security for Canadians by addressing agricultural labour shortages, while tempering food insecurity in the Global South via remittances. Such appeals hinge on an ideology defining migrants as racialized outsiders to Canada. Drawing on qualitative interviews and participant observation in Mexico, Jamaica and Canada, we critically analyse how Canada's Seasonal Agricultural Worker Program is tied to ideological claims about national food security and agrarianism, and how it purports to address migrant workers’ own food insecurity. We argue remittances only partially, temporarily mitigate food insecurity and fail to strengthen migrant food sovereignty. Data from our clinical encounters with farm workers illustrate structural barriers to healthy food access and negative health consequences. We propose an agenda for further research, along with policies to advance food security and food sovereignty for both migrants and residents of Canada.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.216
Threshold uncertainty score0.910

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0380.056
Scholarly communication0.0140.004
Open science0.0050.005
Research integrity0.0100.015
Insufficient payload (model declined to judge)0.0040.000

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 head0.182
GPT teacher head0.449
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations56
Published2017
Admission routes2
Has abstractyes

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