Safe Country of Origin List at the EU Level: The Bargaining Process and the Implications
Bibliographic record
Abstract
Critics have often highlighted that the 1999 Tampere decision to establish a common European Union (EU) asylum system has been too focused on security and not enough on human rights, leading to increased denial of protection for asylum seekers. This paper focuses on a controversial asylum policy, which is part of this debate: the safe country of origin (SCO) policy. This policy revolves around having a list of countries deemed "safe" which ensures asylum seekers from these countries are fast tracked through the system and likely denied asylum in the end, based on a general assumption that the application is unfounded. Human rights groups have argued the SCO policy violates the Geneva Convention. Widely used at the national level, officials proposed the creation of a supranational SCO list in the early 2000s. However, disagreements among Member States over what countries to deem “safe" as well as the need to place the European Parliament in a co-decision (as opposed to consultative) position for the creation of the EU SCO list have led to an impasse. This paper employs two major European integration theories, neofunctionalism and liberal intergovernmentalism, to explain the bargaining dynamics between Member States and their failure to agree on what “safe" means. Factors such as different national migratory pressures, varied procedural understandings and applications of the SCO policy, a limited successful harmonization in related asylum policies, along with a reluctance to have the European Parliament in a co-decision position all contributed to the non-adoption of a supranational SCO list. Full text available at: https://doi.org/10.22215/rera.v7i1.212
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".