Disinformation, Exclusion, and its Politics: Canadian Right-Wing Extremist Community within a Digital Landscape
Bibliographic record
Abstract
Research on right-wing extremism has historically been overwhelmingly focused on the movement’s preoccupations in the United States and Europe. Scholarly literature on Canadian groups and their beliefs has been sparse, with few studies mapping the extent of their activities. Right-wing extremism has captured journalistic attention in recent years as lone-wolf right-wing extremists radicalized on the Internet take up arms against racialized groups they see as anathema to their White supremacist groups’ survival. This research examines right-wing extremist conceptions of out-groups (the ‘Other’) and resulting political demands to contain this imagined threat through a case-study approach of Stormfront Canada. I conducted a thematic analysis of publicly available digital communications exchanged between community members between January 1st and December 31st, 2021. Major themes identified for forum threads were anti-hate initiatives, politics, crime, and health within the COVID-19 context, while for forum replies these were disinformation, offensive speech, and politics. I also quantified the extent of this community’s activity and found that most content shared to the website is posted by less than six active members. This thesis argues that discursive constructions of the Other depend on exclusionary belief systems predicated on support for White hegemony, and that political demands expressed by community members to contain the perceived threat posed by the continued existence of racial out-groups are shaped by an adherence to the Great Replacement superconspiracy.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.046 | 0.013 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".