Quantifying international migration : a database of bilateral migrant stocks
Bibliographic record
Abstract
This paper introduces four versions of an international bilateral migration stock database for 226 by 226 countries and territories. The first three versions each consist of two matrices, the first containing migrants defined by country of birth, that is, the foreign-born population; the second, by nationality, that is, the foreign population. Wherever possible, the information is collected from the 2000 round of censuses, though older data are included where this information was unavailable. The first version of the matrices contains as much data as could be collated at the time of writing but also contains gaps. The later versions progressively use a variety of techniques to estimate the missing data. The final matrix, comprising only the foreign-born, attempts to reconcile all of the available information to provide the researcher with a single and complete matrix of international bilateral migrant stocks. The final section of the paper describes some of the patterns evident in the database. For example, immigration to the United States is dominated by Latin America, whereas Western European immigration draws heavily on Eastern Europe, Central Asia, and the Mediterranean region. Over one-third of world migration is from developing to industrial countries and about a quarter between developing countries. Intra-developed country and intra-FSU (former Soviet Union) flows each account for about 15 percent of the total. Over half of migration is between countries with linguistic ties. Africa accounts for 8 percent of Western Europe's immigration and much less of that to other rich regions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".