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Record W2621078189 · doi:10.60082/0829-3929.1259

The Intersection of Exploitation and Coercion in Cases of Canadian Labour Trafficking

2017· article· en· W2621078189 on OpenAlexvenueaboutno aff
Jesse Beatson, Jill Hanley, Alexandra Ricard-Guay

Bibliographic record

VenueJournal of Law and Social Policy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsCoercion (linguistics)Human traffickingCriminologyPolitical scienceSex traffickingIntervention (counseling)SociologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Internationally, human trafficking intervention, research, and policy-making has leaned towards sex trafficking rather than labour trafficking. Aiming to understand the characteristics of labour trafficking within Canada, a country considered by many to have strong labour protections and clear pathways for labour migration, this article reports on a review of documented cases over the past fifteen years in Canada where labour exploitation intersected with coercion. Our analysis is centred on the notion that this is the crux of what constitutes labour trafficking—coercion being used to facilitate labour exploitation. In total, we collected thirty-six cases, involving an estimated 243 victims, and we placed these within a matrix that crosses gradations of labour exploitation (deception, labour standard violations, and occupational health and safety (OHS) violations) with gradations of coercion (from systemic to direct). We collected these cases through a scan of media, governmental, academic, and legal sources. A new contribution to the literature, this exploitation-coercion matrix helps to highlight limitations in current approaches to the identification and response to labour trafficking in Canada. Our study results demonstrate: 1) the degree to which precarious immigration status is central to labour trafficking; 2) that this trafficking is frequently practised by small business owners in legal employment sectors; and 3) that there is a high presence of men and low presence of minors as victims. These findings contrast with the archetypal portraits found in much of the trafficking media and literature of the trafficking victim as young and female and the trafficker as organized criminal.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.571

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 studies0.0010.000
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 head0.042
GPT teacher head0.355
Teacher spread0.313 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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