Bibliographic record
Abstract
In February 2021 the Canadian government published a considerably expanded list of domestic terrorist entities. While some, such as Blood and Honour, were already known, others – such as Atomwaffen Division, the Base, the Proud Boys, and the Russian Imperial Movement – emerged from the shadows. Until then many considered far-right groups in Canada a negligible phenomenon, at worst a local police matter. The Great Right North charts the growth of these groups, illuminating how official and unofficial government attention generates the context in which they build their movements. The result of seven years of research – including social media scraping, analysis of print and video sources, and interviews with scores of leaders and adherents – it examines how far-right organizations operate, recruit, and finance their activities and explores why individuals choose to join. Breaking new ground by revealing the ideological underpinnings and fragmentation within these groups, the authors also highlight the role of digital platforms in their proliferation. Most politicians have been quiet about the phenomenon of far-right extremism in Canada, insisting it is imported activism financed elsewhere. The Great Right North provides an essential primer – for journalists, those working in policy institutes and think tanks, and students and scholars – for understanding its vast and urgent homegrown challenges.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".