Case management, identity controls and screening on national security and 1F exclusion: A comparative study on Syrian asylum seekers in five European countries
Bibliographic record
Abstract
This report discusses how five European countries (Belgium, Germany, Norway, the Netherlands and Sweden) have organized the identification, registration and decision-making in relation to asylum applications made by Syrian nationals, and the screening of Syrian nationals with regard to possible national security and 1F exclusion aspects, in the period 2014-2017. For the study, interviews have been conducted with representatives of immigration authorities and aliens police agencies, as well as representatives of intelligence and security services and representatives of the European Asylum Support Office (EASO). In addition, the research entailed a review of available academic literature, relevant rules and regulations and available formal and informal policy documents. The armed conflict in Syria that erupted in 2011 has produced a vast number of forced migrants and is one of the driving factors behind the high influx of asylum seekers in Europe since 2014. The high influx impacted all countries studied in the context of this research, albeit in different degrees. The high influx came as a surprise to all of the focus countries, because of its suddenness and its magnitude. The challenges that bureaucracies were confronted with were manifold. This report presents an overview of these challenges and responses to these challenges in the five focus countries, on three main themes: organisational capacity and management; establishment of identity and decision-making; and screening on national security and 1F exclusion. The report ends with a number of conclusions, reflections and recommendations that follow from the findings.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".