Migration data collection and management in a changing Latin American landscape
Bibliographic record
Abstract
Abstract While many Latin American countries have a tradition of receiving migrants, including the countries selected as case studies, there are no institutionalized mechanisms for the integration and settlement of migrants. The objective of this article is to explore how to improve migration data collection and management in a region that does not have many migration integration policies in place. I assess the state of migration data collection and management in three case studies: the city of Cucuta in Colombia, the North Huetar Region in Costa Rica, and the city of Monterrey in Mexico. The three countries publish data exclusively at the national level, rather than the local or municipal. Despite all case studies having a variety of administrative data, mainly in the form of entries and exits by nationality, these data are not enough to properly identify the sociodemographic characteristics of migrant populations in a country, and much less in specific cities. I make recommendations divided into three main themes to improve migration data in Latin America.
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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 | 0.037 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".