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Record W3045330441 · doi:10.1111/imig.12743

Creative Recourse in Cases of Forced Labour: Using Human Trafficking, Human Rights and Labour Law to Protect Migrant Workers

2020· article· en· W3045330441 on OpenAlexaffabout
Laurence Matte Guilmain, Jill Hanley

Bibliographic record

VenueInternational Migration · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsCoercion (linguistics)Labour lawHuman traffickingHuman rightsJurisprudenceConvictionLegislationLawPolitical scienceMigrant workersForced migrationCriminologySociologyEconomicsRefugeeEconomic growth

Abstract

fetched live from OpenAlex

Abstract Canada has no legislation prohibiting forced labour, relying instead on human trafficking penal dispositions; the two are intimately related. However, there has only been one conviction for human trafficking for the purposes of forced labour. Here, we offer an analysis of jurisprudence according to a model of labour trafficking focusing on the intersection of labour exploitation and coercion, particularly among migrant workers. We argue that Canadian human trafficking law remains incomplete and fails to address situations of forced labour. There is a need to think creatively about recourses. Labour and human rights law recourses are more accessible to migrant workers than human trafficking law, given the lighter burden of proof and the unlikelihood that courts will recognize the systemic coercion to which migrant workers are subject. Workers and advocates are understandably drawn to these alternative recourses, yet consequences for employers profiting from forced labour are disappointingly minor.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.946

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 head0.039
GPT teacher head0.350
Teacher spread0.311 · 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 designQualitative
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

Citations6
Published2020
Admission routes2
Has abstractyes

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