International Migration in the Caribbean
Bibliographic record
Abstract
This paper provides a broad overview of the migration phenomenon in the Caribbean based on available evidence. It first describes the migration context in the region, with a focus on key migration related facts, significant trends, and current and future challenges. It then identifies and discusses examples of policies implemented in the region, including evidence on their impact track record. Migration presents both significant challenges and opportunities for the long-term socioeconomic development of the region. The paper’s main takeaways include the following. Mass emigration of working-age individuals is a significant concern for all countries in the region as skills gaps in sectors such as education, health, and information technology have emerged. Empirical evidence and theory suggest that the most effective way to address issues related to brain drain is to increase opportunities and incentives for skilled nationals to stay in the origin country. While some Caribbean countries participate in temporary migration work programs with the United States and Canada, there is scope to increase take-up and the positive impacts of such programs for migrants, origin countries, and destination countries. High remittance transfer costs remain an obstacle for migrants sending money through official channels and limit economic gains for migrants and their families. Migration policy evaluations should be conducted more systematically to enable decision makers to better promote safer and more orderly migration. Last but not least, systematic compilation and analysis of migration data are important gaps to be addressed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".