The Removal of Maher Arar and Lessons Learned for Future Engagement Between the United States and Canada
Bibliographic record
Abstract
Since the terrorist attacks of September 11, 2001, the United States and Canada have engaged at the highest levels of government to integrate immigration and law enforcement policies and achieve common homeland security benefits. This engagement demonstrates agreement across political parties in both countries on those areas and objectives critical to increasing North American security. Over the same period of time, the removal by the United States of Canadian citizen Maher Ararbased in part on derogatory information provided by Canadian law enforcementillustrates vividly the complexity, sensitivity and necessity of informal collaboration between agencies in both countries. This thesis presents a case study of the removal of Mr. Arar in order to suggest strategies that policymakers in both countries may adopt in order to achieve greater progress toward the objectives identified during bilateral engagement over the past decade. This thesis relies on the unclassified results of official inquiries in the United States and Canada as well as the record developed by related litigation in both countries, and concludes that this incident itself continues to prevent further integration between the United States and Canada and should be addressed squarely to achieve greater progress toward bilateral security objectives.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".