Tools for collecting information on irregular migration estimates and indicators
Bibliographic record
Abstract
This paper discusses the tools used to collect quantitative data related to irregular migration stocks and flows of the Measuring Irregular Migration and Related Policies (MIrreM) project. The ultimate goal of this exercise was to construct two databases that provide an inventory and a critical appraisal of estimates and indicators related to irregular migration in the countries covered by MIrreM (12 EU member states, the UK, Canada, the USA and five transit countries). The databases contain estimates on the size and characteristics of the irregular migrant population in a given country and the changes in that population, with one database focussing on irregular migrant stocks and the other on flows. The flows database also contains an inventory of other indicators of irregular migration (e.g. border apprehensions). MirreM is a follow-up project to the Clandestino project which covered the period 2000-2007. MIrreM covers the period 2008 to 2023. MIrreM guidelines were adjusted from those developed by the Clandestino project to maintain some consistency across projects, but also to account for changes across the different periods and overall purposes of the projects. In addition, the approach to assessing the quality of estimates and indicators was refined, notably by explicitly distinguishing between statistical indicators, on the one hand, and estimates, on the other, developing different assessment criteria, and collecting information on the use of these data in policymaking. Beyond the immediate purpose of guiding data collection and analysis within the MIrreM project, these tools may also be useful for other researchers working on comparable topics characterised by a lack of robust research-driven data, hard-to-reach target groups and limited and imperfect administrative data.
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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 | 0.065 | 0.245 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.049 | 0.036 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".