Migrant access to social protection under Bilateral Labour Agreements a review of 120 countries and nine bilateral arrangements
Bibliographic record
Abstract
This working paper: (i) examines migrants access to social protection under Bilateral Labour Agreements (BLAs) with a view to providing policy makers with guidelines for extending social protection to migrants and designing better migration policies; (ii) presents the results of a mapping of bilateral and multilateral social security agreements in 120 countries; (iii) reviews legislation with respect of the provisions granting equality of treatment between nationals and non-nationals; (iv) provides a more in-depth legal analysis of migrant workers’ access to social protection under BLAs or Memoranda of Understanding (MoUs) for 9 corridors, 15 countries, namely: Canada-Mexico, Spain-Morocco, Spain-Ecuador, France-Mauritius, France-Tunisia, Philippines- Saudi Arabia, Qatar-Sri Lanka and Republic of Korea-Sri Lanka, South Africa- Zimbabwe, as well as migrant’s access to social protection in Belgium; (v) promotes the inclusion of social security provisions into BLAs and MoUs ensuring the organization of migration for employment, in particular provisions on equality of treatment with respect to social security; and (vi) calls on policy makers to ratify and apply relevant international labour standards, conclude multilateral and bilateral social security agreements, adopt unilateral measures to enhance migrant workers’ access to social protection, involve social partners in the design and implementation of social protection for migrant workers, and take action to tackle the practical barriers migrant workers and their families face to be able to fully enjoy their right to social security.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".