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Record W7034324080

The Temporary Mexican Migrant Labor Program in Canadian Agriculture

2004· article· en· W7034324080 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureImmigrationWork (physics)WageGovernment (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

During the early years of the Program (1974)(1975)(1976)(1977)(1978)(1979)(1980), there was not much promotion for recruiting workers, and this was done only in states near Mexico City.By 1994, 80% of the participants came from six states in the central part of the country: Puebla, Tlaxcala, México, Morelos, Hidalgo, and Guanajuato.With the increase in the demand for workers and the decentralization of certain procedures for selecting and documenting workers, these have been incorporated from all the states.However, 70% of the participants still come from the central region of the country.Since 1974, the year in which the program of Mexican workers began, the number of participants has increased on an average by 18% annually.This growth has been determined by Canadian employers' demand for workers: the periods showing the greatest increases were 1985 to 1989 and 1996 to 2000.Nominal workers account for 48% and 68%, respectively, of the total number of workers going to Canada each season.The year 1989 was the first one in which Canadian farmers requested women workers through this Program.At present, women's participation in the total number of workers per season is around 3%.Although these numbers are very low, it is clear that women's participation in the Program has more than doubled in just a few years.This is due, above all, to an increase in the demand among Canadian employers, so that the women who have participated during all the seasons are the ones who are explicitly requested by their gender. Operation of the ProgramThroughout the years, several changes have been made to improve the Program's operation.A "single-window" system was set up to facilitate procedures, allowing workers to conduct most of the procedures in the Program Office without

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.011
GPT teacher head0.235
Teacher spread0.224 · 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 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

Citations0
Published2004
Admission routes1
Has abstractno

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