Bibliographic record
Abstract
After the terrorist attacks of September 11, 2001, Canadian agencies willingly collaborated in the War on Terror launched by the United States to destroy Al Qaeda. This partnership went seriously astray, however, amid a series of fundamental errors by Canadian agencies and their misplaced trust in American willingness to abide by both international and US laws against torture. As a result, numerous Canadian citizens and residents were illicitly detained abroad and subjected to suffering and mistreatment. In Detained Daniel Livermore analyzes the emergence of Islamic fundamentalist extremism and its Canadian implications, including the erroneous investigations that targeted Canadians and led to their detentions in Syria, Egypt, Pakistan, Libya, Tunisia, and Sudan. Scrutinizing the most prominent cases, he details the role of Canadian agencies in the imprisonments and relates how subsequent court cases brought the situations to light, resulting in settlements and apologies to Ahmad Abou-El-Maati, Abdullah Almalki, and Maher Arar, among others. Drawing on his experience in Canada's foreign ministry, Livermore explains how an essentially misguided War on Terror emerged and how Canadian-American cooperation went wrong. A gripping blend of memoir and meticulous research, Detained urges a more mature and rational discussion of security and intelligence issues in Canada and greater understanding of the failures of security cooperation in the decade after 9/11.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.018 |
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".