The migration ban policy cycle: a comparative analysis of restrictions on the emigration of women domestic workers
Bibliographic record
Abstract
Abstract Policies banning women domestic workers from migrating overseas have long been imposed by labour-sending states in the Indo-Pacific region. This article presents the complexities surrounding such bans by developing an overarching model of a migration ban policy cycle, which provides a theoretical framework for understanding the circumstances under which migration bans arise and play out. It examines the history of migration bans for four prominent labour-sending states – Indonesia, Nepal, the Philippines and Sri Lanka - to assess the causes, outcomes and extent of regional convergence of these policies. In doing so, we uncover two prominent policy narratives. The first involves labour diplomacy, where countries employ bans to negotiate superior working conditions and rights for migrant workers. The second concerns paternalist states as ‘protector’, where states are primarily motivated to reaffirm traditional gender norms. We conclude that migration bans have been most effective, both in curbing departures and achieving desired outcomes, when they are primarily motivated by labour issues and not gender politics. Nevertheless, even when used as a form of diplomatic negotiation, migration bans heighten the vulnerability of domestic workers to exploitation by pushing them into irregular pathways fraught with risk.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".