Challenge and response: Canada's intelligence community and the war on terrorism
Bibliographic record
Abstract
The terrorist attacks of September 11th catapulted Canada's intelligence community to the forefront of our national security effort. Faced with the danger of international Jihadist terrorism, Canada's intelligence community could no longer remain in an essentially reactive mode. Rather, they were impelled to shift gears to become active hunters after their quarry. The article describes the evolving governance structure of Canada's Security and Intelligence Community, culminating in the organizational changes implemented by the new Martin Government after December 2003. It then assesses the terrorist threat to Canada, setting out the presence of major terrorist networks and cells, including al‐Qaeda and its affiliates, and their known activities and objectives. There follows an examination of the roles of each of the components of Canada's intelligence community, CSIS, CSE, the RCMP, FINTRAC, OCIPEP, and specialized departmental units in dealing with the terrorist threat. Consideration is also given to the new legislative armoury provided by the Anti‐Terrorism Act of 2001 and supporting regulations. Since international intelligence relations play a significant part in Canada's counter‐terrorism effort, the article sets out the various inter‐governmental and bilateral arrangements through which intelligence sharing and operational cooperation take place. The concluding section addresses the challenges faced by democracies in combating international terrorism, including the need for accountability, transparency and public education consistent with the requirements of operational secrecy, in order to build up public confidence in Canada's emergent National Security Policy.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.012 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".