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Record W2294728882 · doi:10.1504/ijmbs.2014.068967

The recruitment of Guatemalan agricultural workers by Canadian employers: mapping the web of a transnational network

2014· article· en· W2294728882 on OpenAlexaffabout
Dalia Gesualdi Fecteau

Bibliographic record

VenueInternational Journal of Migration and Border Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsImmigrationContext (archaeology)Work (physics)AgricultureBusinessPhenomenonField (mathematics)Demographic economicsLabour economicsPolitical scienceLawEconomicsGeographyEngineering

Abstract

fetched live from OpenAlex

For the past two decades, the number of immigrants admitted to Canada has remained relatively stable while the number of workers admitted with a temporary work permit has steadily increased. This phenomenon is explained by a shift of the Canadian public policies that foresees the management of labour migration. Given their foreignness, the conditions through which temporary foreign workers are employed are a result of the 'dialogue' between labour law and the rules issued by immigration law. Different tensions stem from the coexistence of these two regulatory sets which do not ensure the same functions, are not aimed at the same purpose and are not developed nor implemented by the same actors. This paper will expose the results of a field study conducted between 2012 and 2013. Our research allowed us to better understand the context in which Guatemalan seasonal agricultural workers hired via the agricultural stream of the Canadian temporary foreign workers program are recruited. This research also sheds light on the complex and highly ramified transnational network that allow these workers to be hired by employers in Canada and which exercises a definite influence on the parties to the employment contract.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.515

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 communication0.0000.000
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 head0.048
GPT teacher head0.336
Teacher spread0.288 · 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 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

Citations7
Published2014
Admission routes2
Has abstractyes

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