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Record W2964566017 · doi:10.1177/1048291119867757

Factors Influencing the Health and Safety of Temporary Foreign Workers in Skilled and Low-Skilled Occupations in Canada

2019· article· en· W2964566017 on OpenAlexafffundabout
Leonor Cedillo, Katherine Lippel, Delphine Nakache

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2019
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBusinessVulnerability (computing)ImmigrationOccupational safety and healthWorkloadWork (physics)HospitalityLabour economicsFamily reunificationCompliance (psychology)Job securityDemographic economicsPolitical sciencePsychologyEconomicsEngineeringTourismComputer security

Abstract

fetched live from OpenAlex

This article reports on a study of occupational health and safety (OHS) challenges for temporary foreign workers (TFWs) in low- and high-skilled occupations, based on twenty-two cases drawn from a broader study in three Canadian provinces. Interviewees in construction, meat processing, hospitality, and fast food reported concerns regarding working conditions and OHS issues. They include precarious migration status affecting voice; contrasting access to social support; and mechanisms undermining regulatory effectiveness. Sources of vulnerability include closed work permits (making workers dependent on a single employer for job security and family reunification); ineffective means to ensure contractual compliance; and TFW invisibility attributable to their dispersal throughout the labor market. Violations include increased workload without an increase in pay and non-compliance with OHS and contractual rules without oversight. Positive and negative practices are discussed. Recommendations include improving migration security to preserve worker voice and facilitating communication between immigration and OHS authorities.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.950

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 studies0.0010.000
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 head0.038
GPT teacher head0.355
Teacher spread0.317 · 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 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

Citations35
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueNEW SOLUTIONS A Journal of Environmental and Occupational Health PolicySame topicEmployment and Welfare StudiesFrench-language works237,207