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Record W2091298355 · doi:10.1371/journal.pmed.1001043

Migration and "Low-Skilled" Workers in Destination Countries

2011· article· en· W2091298355 on OpenAlexaff
Joan Benach, Carles Muntaner, Carlos Delclós, María Menéndez, Charlene Ronquillo

Bibliographic record

VenuePLoS Medicine · 2011
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDeportationMigrant workersOccupational safety and healthFlexibility (engineering)BusinessWork (physics)Human migrationEconomic growthImmigrationDemographic economicsPopulationEnvironmental healthPolitical scienceMedicineEconomicsLaw

Abstract

fetched live from OpenAlex

Summary Points
\n
\nThe last few decades have seen substantial increases in the migration of migrant workers, raising concerns about the health implications of these movements.
\nIn destination countries, migrant workers often fill undesirable, low-skill positions characterized by flexibility, insecurity, precarious employment, and long working hours with low pay.
\nUndocumented or “illegal” migrants are especially vulnerable to exploitation since they fear job loss, incarceration, and deportation.
\nUrgent health issues to be addressed among migrant workers include occupational safety, injury prevention, work-related diseases, barriers to accessing health services, and the associated health risks for their families and communities, in addition to discrimination and exploitation.
\nGovernments, unions, and international organizations should collaborate to implement fair labour standards for both legal and illegal labourers that are on par with citizen workers, standardise labour migration policies, and provide legal support for undocumented labourers to help eradicate human trafficking and other forms of extreme labour exploitation.

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)0.000
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 score0.022
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.085
GPT teacher head0.374
Teacher spread0.289 · 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

Citations110
Published2011
Admission routes1
Has abstractyes

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