How Canadian Intelligence is Exposed to the Impact of Globalization: A Critical Analysis of the Security Threat of Right-Wing Extremism
Bibliographic record
Abstract
It could be argued that Canadian intelligence has been negligent toward the impact of globalisation when assessing the security threat of right-wing extremism (RWE), specifically with the advent of the internet and the significant reduction of the influence of state borders on national policy objectives, and therefore has exposed itself to the potential of intelligence failure. This paper is focused on the state of right-wing extremism in Canada through which it addresses the security question: How is Canadian intelligence exposed to the impact of globalisation? The results of this paper are informed by an in-depth analysis of peer-reviewed articles from Canada, the United States (US), and Europe, as well as Canadian government documents, and newspaper articles, as well as the completion of a key assumptions, check to address bias and better evaluate the evidence found. This paper concludes that it appears likely that Canadian intelligence may not be assessing RWE threats through the lens of globalisation. It could be argued that this creates the potential for intelligence failure. However, there remains one significant caveat. It can be interpreted in Public Safety Canada’s latest update that RWE may soon be considered a type of terrorism. If this is the case, the evidence proves that Canadian intelligence may in fact be considering the impact of globalisation in the context of terrorism and therefore would likely implement the same consideration for RWE.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.033 | 0.015 |
| Scholarly communication | 0.023 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".