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Record W3029726627

Who, How and How Much? Recruitment of Guatemalan Migrant Workers to Quebec

2017· article· en· W3029726627 on OpenAlexfundaboutno aff
Dalia Gesualdi‐Fecteau, Andréanne Thibault, Nan Schivone, Caroline Dufour, Sarah Gouin, Nina Monjean, Éloïse Moses

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMemorial University of NewfoundlandUniversité du Québec à Montréal
KeywordsFocus groupIntermediaryQualitative researchAgricultureWork (physics)Political scienceEconomic growthSocioeconomicsGeographyBusinessSociologyMarketingEngineeringSocial scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

The main aim of this report is to inform policymakers and the public about the conditions under which temporary foreign workers (TFWs) are recruited to work in agriculture in Quebec, Canada and the role that recruitment intermediaries play in that process. This report presents the results of a field study conducted between June and November 2015 that sought to document
\nrecruitment practices in Guatemala of Guatemalan agricultural workers recruited to Quebec, and suggests steps that can be taken to improve this process. The mixed-methods approach combined qualitative and quantitative research including semi-directed interviews, focus groups and an anonymous survey.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.060
GPT teacher head0.261
Teacher spread0.201 · 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.

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

Citations2
Published2017
Admission routes2
Has abstractyes

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