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Record W2033642133 · doi:10.1080/01436590902742297

From Fields of Power to Fields of Sweat: the dual process of constructing temporary migrant labour in Mexico and Canada

2009· article· en· W2033642133 on OpenAlexaboutno aff
Leigh Binford

Bibliographic record

VenueThird World Quarterly · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)AttractivenessPower (physics)Dual (grammatical number)SociologyMigrant workersFactory (object-oriented programming)Labour economicsEconomic growthPolitical scienceEconomicsGeography

Abstract

fetched live from OpenAlex

This article examines the social construction of migrant labour forces through an analysis of the exterior and interior conditioning in an agricultural contract labour programme between Mexico and Canada. I argue that forms of exterior conditioning, especially employers' point-of-production control, establishes the context within which migrant workers' experience unfolds, for which reason it contributes to their ‘interior conditioning’. But I argue as well that the result is shaped by workers' employment of a ‘dual frame of reference’ through which they gauge Canadian wages and working conditions the only way they can, which is in relationship to Mexican ones. Given that neoliberal policies have reduced the options available in Mexico, and diminished the attractiveness of those that remain, contract labour in Canada presents one of the few opportunities many poor, rural Mexicans have to acquire the income necessary for a minimally dignified life. Consequently most workers in this programme do everything possible to please their employers and continue in the programme.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0220.010
Scholarly communication0.0050.001
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.255
Teacher spread0.248 · 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 designQualitative
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

Citations81
Published2009
Admission routes1
Has abstractyes

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