Introduction: Refugees and Asylum Seekers in the International Context – Rights and Realities
Bibliographic record
Abstract
This book uses the idea of the ‘Rule of Law’ to illuminate how the legal systems in five industrialized countries which share a common legal heritage – namely Canada, the USA, Australia, New Zealand and the UK – have responded to issues about the rights and status of refugees and asylum seekers. This is a particularly important issue in its own right because, as explained in Chapter 1, the rule of law, which is the cornerstone of the concept of democracy and of modern legal systems, is challenged by its application to refugees and asylum seekers. It is also important for a second reason. The rights of refugees are defined in international law, but are subject to state discretion as to their implementation in national legal systems. As the chapters in this book reveal, implementation is being done in such a way as to deny refugees the rights which are due to them under the international regime of refugee protection. Thus the international rule of law is also being eroded by implementation at the national level. This raises the issue of the interconnectedness of the national and the international rule of law. Should the rule of law be regarded only as a ‘national enterprise’, as Audrey Macklin queries? How can coherency between the national and the international rule of law be achieved? These are issues addressed in this book.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".