Bibliographic record
Abstract
From September to December 2010, the Canadian Security Intelligence Service (CSIS) conducted a study using the twenty-four people charged and/or convicted under provisions contained with the Anti-terrorism Act (ATA) as a control group to analyze the factors that led to the political transformation of these individual actors. This study, which is entitled “A Study of Radicalisation: The Making of Islamist Extremists in Canada Today,” reaches some of the following conclusions: the majority of Domestic Islamitic Extremists demonstrate a high degree of integration in mainstream Canadian society; these same actors possess heterogeneous ethnic, family, and socio-economic backgrounds; the majority of these actors are highly educated and have no history of violent criminality; and, ultimately, that there is no reliable profile of Domestic Islamitic Extremist actors. 1 As a result, according to this study, the identification of readily discernible “patterns and trends on radicalisation remains elusive.” 2 Subsequent to the public release of this study, Doug Saunders, in an article entitled “We’re looking for terrorists in all the wrong places,” makes the following observation after synthesizing the findings of the CSIS report and similar reports conducted by MI5 and the New York Police Department (NYPD): These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".