Bangladeshi labour migration to the Gulf states: patterns of recruitment and processes
Bibliographic record
Abstract
The Gulf countries in the Middle East are one of the largest regions relying on international labour migrants for economic development. Recruitment constitutes an important part of this migration of labour. This study addresses the complexity and multiplicity of labour recruitment in the Gulf countries through a case study of Bangladeshi labour recruitment. This study examines the labour recruitment to the Gulf, combining networks and institutions to highlight both the operational and economic aspects of migrant recruitment. This article reveals how migrant networks and recruitment agencies adapt to the changing practices of recruitment to funnel migrant workers to the GCC countries and make profits out of the migrant workers in the recruitment process. Résumé Les pays du Golfe constituent une des plus grandes régions reliant sur la migration de main d'œuvre pour le développement économique. Le recrutement joue un rôle important dans cette migration. Ce travail addresse la complexité et la multiplicité du recrutement dans les pays du Golfe en analysant un cas d'étude au Bangladesh. Cette étude examine le processus de recrutement, particulièrement les réseaux et les institutions qui soulignent les aspects opérationaux et économiques de la migration. En plus, cette étude révèle l'adaptation des réseaux de migration et des agences de recrutement aux pratiques changeants dans le recrutement ainsi bien que les stratégies employées pour assurer leur profit.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".