Labour Migration in Latin America and the Caribbean Diagnosis, Strategy, and Ilo's Work in the Region
Bibliographic record
Abstract
As in the rest of the world, in Latin America and the Caribbean migration has been expanding in volume, dynamism, and complexity over the past decades and is closely linked to the world of work and the quest for employment, income, and decent work opportunities. Today, almost all countries in the region are part of migration flows, whether as countries of origin, transit or destination. The United States of America continues to be the main country of destination for most Latin America and Caribbean migrant workers and their families. Yet, migration has become more diverse and intense in intra-regional migration corridors to countries like Argentina, Barbados, Brazil, Costa Rica, Chile, Dominican Republic, Panama, and Trinidad and Tobago; as well as in inter-regional corridors, particularly to countries such as Canada, Spain, Italy, and Portugal. This report contains a diagnosis, as updated as possible with available information from major migration corridors in the American continent, intra and inter-regional wise. It describes the common features of these corridors; analyses the weaknesses and challenges of public policies and governance in such corridors; and describes the progress, good practices, and opportunities to improve labour migration in the region. Based on the above-mentioned diagnosis and the institutional mandate of the ILO, the last section presents ILO’s strategy and lines of work in labour migration to be implemented in the Latin American and Caribbean region during the 2016-2019 period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".