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):
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".