The Emperor’s New Clothing: National Responses to “Undesirable and Unreturnable” Aliens under Asylum and Immigration Law
Bibliographic record
Abstract
The “scandal” of foreign criminals whom our governments cannot send back to their own countries has become something of a tabloid obsession. Yet, while suspected or convicted of serious crimes or considered to pose a danger to society, such “undesirable and unreturnable” aliens equally often languish in an ambiguous and even dangerous state of protracted legal “limbo”, lacking a defined immigration status and attendant access to basic rights in the host State. In the absence of an agreed common framework for resolving this anomalous situation, how do individual States deal with the legal and policy paradox that is embodied by these purportedly “undesirable”, but also ultimately non-removable, aliens? This Special Issue offers a preliminary perspective on this contemporary concern by presenting eight specially commissioned pieces of new research. Each of the contributions examines a different national context where this issue has arisen in recent years, resulting in eight detailed country case studies covering Australia, Canada, France, Greece, India, the Netherlands, Turkey, and the United Kingdom. The aim is to produce a comparative understanding of national responses in this relatively diverse range of countries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".