MIrreM Public Database on Irregular Migration Flow Estimates and Indicators
Bibliographic record
Abstract
This Public Database on Irregular Migration Flow Estimates and Indicators, in short MIrreM D5.2, is a MIrreM project deliverable under work package 5. This database provides an inventory and critical appraisal of available estimates and indicators related to irregular migration flows. More specifically, the database contains the country-level data collected by MIrreM’s national rapporteurs, as well as EU-level data from sources other than Eurostat. The datasets include meta-level information on sources and methodology and a quality assessment based on MIrreM’s criteria. Users of this database are advised to consult the following companion document (henceforth, MIrreM Working Paper No. 9/2024) for a full discussion of the context, the underlying concepts, and the methodology used: Siruno, L., Leerkes, A., Hendow, M. & Brunovská, E. 2024. Working Paper on Irregular Migration Flows. MIrreM Working Paper No. 9. Krems: University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo. 10702228. The MIrreM project is a follow-up to CLANDESTINO, which covered the period 2000-2007. MIrreM extends this to the subsequent period 2008-2023. The data covered in this database reflect what is available within this period. Most of the data was collected between June and October 2023, and thus in some cases, the data are only until 2022 pending complete reports for 2023.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 0.096 |
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".