Canada’s Right-Wing Extremists: Mapping their Ties, Location, and Ideas
Bibliographic record
Abstract
Canada has often been seen as a progressive country that is welcoming to immigrants, promotes multiculturalism, and generally as a kind and tolerant society. This study used a two-month close examination of Canada’s RWE online presence surrounding the 2019 federal election. Using social network analysis, this study fills a needed empirical gap in current understanding of this network that are known to produce and sustain domestic terrorism and extremist hate crimes in Canada. Then using both discourse and correspondence analysis, we find that Canada’s Right-Wing Extremists (RWEs) galvanize around the following key ideas: leftist-propensities towards violence, projecting especially views against the Antifa, anti-immigration, media corruption and dishonesty, anti-elite and anti-establishment values, anti-liberalism, populism, anti-LGBT, anti-environmentalism, biological determinism, white victimization, and anti-consumerism. By determining Canadian RWE’s ties, location and ideas our findings reveal that many RWE leaders are seen as authoritative for their views in the network and create content and community, potentially inciting active participation. As social contagion theory reminds us, these authorities in the RWE network may inspire others into concrete violent action and are of great concern to public safety.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 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".