Europe’s power to influence the laws and practice of international protection worldwide
Bibliographic record
Abstract
This chapter explores Europe’s normative power in refugee law understood to mean the power of the European Union (EU) to influence the laws and practice of countries outside Europe1. The chapter is partly based on a collaborative project, which examined both the extent and the processes of emulation of EU asylum law. The regions and countries considered as case studies spanned across 5 continents: North America (United States, Canada), Latin America (Colombia, Ecuador, Panama and Venezuela), Africa, Europe (EU, Israel, Switzerland) and Australia2. The research project explored the worldwide emulation of key norms and trends of the Common European Asylum System (CEAS), through transnational processes and actors operating within and across domestic borders (such as legislators, regulators, judges and interest groups). In particular, was tested the hypothesis that the European protection regime, being one of the most advanced in the world and covering 25 countries3, is bound to exert considerable influence in other regions. Thus, one may see a « ripple effect » or « trickling effect » far beyond Europe4. This state of affairs raises a number of key questions: How is this happening? How are laws and practice on refugee protection migrating, where to, what happens to them once in their new environment, and why is this happening?
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".