Compilation of 20 Country Briefs on Irregular Migration Policy Context
Bibliographic record
Abstract
This deliverable provides an overview of the national policy landscape related to migrant irregularity for 15 countries in the form of Country Briefs. Briefs are provided for the following countries: Austria, Belgium, Canada, Finland, France, Greece, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, Türkiye, the United Kingdom and the United States. The Briefs are drawn from more extensive analysis conducted within MIrreM Country Profiles, which themselves will be analysed comparatively in a subsequent Deliverable (D3.3). The purpose of the Briefs is to provide a concise synopsis of the main policy issues of relevance for the irregular migration situation in each respective country, with a focus on developments since 2010. They are not foreseen to be an exhaustive representation of the entire policy and irregular migration situation in each country, but rather provide a snapshot of the most relevant issues to date, by each national rapporteur's assessment. Each Brief provides a short overview of the recent (since 2010) policies of relevance for irregular migration, in particular the respective country's irregular migration-related policy priorities, stakeholders, recent policy measures, main turning points in policy development, policy impacts, and challenges in implementation. Moreover, each Brief outlines the main types of migrant irregularity that emerge and the pathways into and out of irregularity in each country, including regularisations as relevant. Annexed to this Deliverable is also an overview of the legal and policy frameworks highlighted as relevant for migrant irregularity in each respective country.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.149 | 0.047 |
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".