Refugee Law After 9/11: Sanctuary and Security in Canada and the United States
Bibliographic record
Abstract
Common wisdom suggests that the 9/11 terrorist attacks changed everything about the character of refugee law in the United States, and even in neighbouring Canada. But did they? And if so, how do the responses of the two countries, including heightened security and more pronounced security anxieties, compare in terms of refugee rights? Refugee Law after 9/11 undertakes a detailed, systematic examination of available legal, policy, and empirical evidence to reveal a great irony: refugee rights were already so whittled down in both countries before 9/11 that there was relatively little room for negative change after the attacks. It also shows that the Canadian refugee law regime reacted to 9/11 in much the same way as its US counterpart, raising significant questions about the power of security relativism and the cogency of the Canadian and US national self-image. Obiora Okafor explores the logic behind changes in refugee law in Canada and the United States following 9/11 and up to the present, uncovering the reasons for the orientation of their respective refugee rights regimes in specific ways. Scholars, students, policy makers, bureaucrats, migration/refugee workers, human rights workers, and all those concerned with refugee law, human rights, and national security will find this book necessary reading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.038 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".