The Inequality of Low-Wage Migrant Labour: Reflections on<i>PN v FR</i>and<i>OPT v Presteve Foods</i>
Bibliographic record
Abstract
Abstract This article explores the inequality inhering to low-wage migrant labour and critically evaluates the current capacity of human rights law to account for and address this inequality. This article uses two recent human rights tribunal decisions as case studies through which to conduct this examination:PN v FR, 2015 BCHRT 60, andOPT v Presteve Foods Ltd, 2015 HRTO 675. While these cases establish the positive role of human rights law in accounting for the wider context in which inequality impacts on migrant labour, this role is also inherently limited by the purpose, scope, and function of the Tribunals. This article will identify and discuss issues illustrated in the cases that are reflective of deeper systemic and structural inequalities attending low-wage migrant labour, including: the underlying reasons motivating low-wage labour migration; the legal regulations governing migrant workers’ status and employment conditions; and, the racialization of migrant workers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.034 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".