The Evolution of Safe Third Country Law and Practice
Bibliographic record
Abstract
Abstract This chapter details how States and regions use safe third country (STC) practices to deny protection to asylum seekers and refugees on the grounds that they have, or may have, protection in another country. The STC notion originated in Switzerland in 1979, spread throughout Europe in the 1980s, and was adopted by the European Union and countries such as Australia and Canada in the 1990s. Since then, developments in STC law and practice globally include new bilateral agreements, reforms to STC provisions in domestic and supranational legislation, and landmark decisions of superior courts. The chapter studies these changes in Europe, Australia, and North and South America, focusing in particular on the period from 2010 to 2020. It argues that there has been a dilution of STC protection standards in these four regions. The thresholds for effective protection have diminished and are lower than the minimum laid down in international treaties. Moreover, in the introduction and evolution of these STC practices, lawmakers and judges have disregarded the legal principle of international solidarity. While STC practices have long been critiqued as burden-shifting rather than -sharing, new STC law and jurisprudence exacerbates inequities between States with respect to responsibility for hosting refugees.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".