Protecting the rights of Filipina Migrant Workers: An analysis of bilateral labour agreements between the Philippines and Canada
Bibliographic record
Abstract
The Philippines is one of the largest labour sending countries in the world, sending a large number of migrant workers abroad every year, including Canada. As such, the Philippines has created and implemented labour export policies to address the protection of its workers’ rights abroad. One of the tools the Philippines utilizes to ensure this are bilateral agreements with a select number of receiving countries. This paper will examine how pursuant Canadian bilateral agreements are with respect to Filipina migrant workers rights as established in the The Migrant Workers and Overseas Act. This will be achieved through the analysis of bilateral labour agreements between the Philippines and Canada in reflection of the Philippines’ labour migration export policies, namely the Migrant and Overseas Workers Act. By conducting a policy analysis of the Memorandum of Understandings (MOU) between the Philippines and Manitoba, British Columbia, Saskatchewan, Alberta, and Yukon, this paper highlights the lack of enforcement of the protection of Filipino migrant workers’ rights and gender sensitive elements in the agreements, important elements of the Migrant and Overseas Workers Act. The reality of Filipina workers’ rights in Canada are discussed despite the existence of these MOUs. Keywords: The Philippines, TFWP, labour migration, immigration, gendered migrant rights, bilateral labour agreements, labour export policies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".