Far-Right Incursions on Canadian Postsecondary Campuses 2012-2022: A Qualitative Content Analysis
Bibliographic record
Abstract
A qualitative content analysis of publicly reported attempts of far-right movements to establish presences on Canadian postsecondary campuses is provided to understand these movements’ tactics and targets. 56 cases across 26 Canadian postsecondary campuses were identified between 2012-2022. Common tactics involved attempts to form white student unions on campuses, as well as physical posters and graffiti promoting white pride, white supremacy, and often hate toward a specific demographic group. Groups that were targeted the most included Black, Indigenous, and Jewish students. Anonymous social media platforms allowed for grassroots far-right movements in Canada to be inspired by their counterparts in the United States and use the same content. Near the end of the study period, tactics became less frequent but more violent, evolving into threats of physical harm and disrupting campus events, which suggests that far-right student movements are growing more extreme. While these attempted farright incursions were for the most part successfully resisted by campus communities, far-right student movements need to be viewed as a security threat. Preventing future far-right surges and promoting post-incursion healing may involve intercultural dialogue events to foster communication about social justice issues and rehabilitation. Strong extracurricular participation can help ensure such events are effective.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".