Chapter 3 – Criminological Perspectives on the Toronto 18
Bibliographic record
Abstract
Historically, research in terrorism studies has drawn from a variety of disciplines including, but not limited to, political science, psychology, and security studies. More recently, however, researchers have argued that criminological approaches can and should inform terrorism studies as well. In this chapter, we apply four criminological perspectives to the case of the Toronto 18: the general strain theory of terrorism, social learning theory, situational crime prevention, and situational action theory. Drawing from news media accounts and court documents as well as extensive personal and background details about the offenders, we examine what inspired members of the Toronto 18 to join the cell, as well as the internal dynamics of the cell and why they selected certain targets, all through a criminological lens. The complexities of the Toronto 18 cases clearly demonstrate why it would be unrealistic at best, and foolhardy at worst, to expect any single orientation to “explain” terrorism. But used in concert, criminological theories and perspectives clearly have a role to play in advancing our understanding of the dynamics of terrorism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".