How countries manage migration data : evidence from six countries
Bibliographic record
Abstract
With migration data in the global spotlight thanks to processes such as the Global Compact for Safe, Orderly and Regular Migration and others, this report aims to provide a comprehensive picture of how migration data systems work in practice at the national level. IOM’s Global Migration Data Analysis Centre carried out an international comparative study to understand better how countries are meeting a range of different migration data challenges, focusing on six case study countries. The objective of the study is to explore how far countries have been making progress in improving migration data and to understand the practical challenges facing national statistical offices and other stakeholders, the impact of frameworks such as the 2030 Agenda for Sustainable Development and the Global Compact for Migration, and more. This first-of-its-kind report on migration data at the global level will help authorities better identify data capacity-building needs and opportunities related to migration data, and it presents a key opportunity to showcase good practices among countries and practitioners. The report is based on interviews with stakeholders in six countries (Canada, Djibouti, Ireland, Jamaica, the Republic of Moldova and Nigeria), including representatives from national statistical offices, line ministries and academia. The report contains six short migration data country profiles and national-level findings, along with a global-level section synthesizing these and discussing the overall implications for the migration data landscape.
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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 | 0.033 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".