Threat of ISIS-Affiliated Foreign Terrorist Fighters Towards Canadian National Security
Bibliographic record
Abstract
Since the loss of approximately 98% of their territory, Islamic State of Iraq and Syria (ISIS) has become a shadow of its former self. Foreign recruits must now decide whether to leave the region and return home or stay and possibly continue the fight. Those that return constitute a threat to their home environments because of their maintained allegiances to ISIS, state of radicalization, post-traumatic stress disorder (PTSD), and improved lethality, and operational effectiveness in conducting acts of domestic terrorism. As a result, this article calls for the development and application of a prosecutorial-reintegrative model that is based off prior research on Foreign Terrorist Fighters (FTFs). The model should also be informed by up-to-date research in the field of deradicalization and disengagement and should consider the different classes of FTFs. Following an understanding of the qualitative differences of ISIS FTFs compared to past foreign fighters, the article concludes that directed counter violent extremist messaging and components of Saudi Arabia’s successful Prevention, Rehabilitation, and After Care (PRAC) program must be factored into any framework for rehabilitation and reintegration while Criminal Code and Anti-Terrorism Act provisions, complemented by evidence gathering strategies, are primarily used to hold FTFs accountable for serious offences.
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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 | 0.004 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".