The Mobilization of Individuals Towards the Radical Right in Canada: A Pathway Approach
Bibliographic record
Abstract
On January 6, 2021, the U.S. capitol came under siege by radical right individuals unhappy with the outcome of the U.S. elections held in November 2020. Following that incident, Canada added multiple radical right groups to its national terrorist entity list. Despite the addition of these groups to a list that was criticized for mostly focusing on Islamist groups post 9/11, the issue of the radical right remains under-researched in Canada. Drawing on life-story interviews and participant observation data, this dissertation seeks to identify the pathways that help explain the mobilization of individuals towards the radical right entities in Canada. I argue that individuals decide to mobilize towards a radical right entity in Canada by conducting a cost-benefit analysis of mobilization. At the time of mobilization, certain political or social macro-level issues interact with an individual’s life at the micro level in a negative manner, and they feel that impact personally. At that moment, if they calculate that the cost of not doing something is higher than the cost of doing something, they take the initiative to mobilize. Using the framework of Bates, De Figueiredo, Jr., and Weingast (1998), I argue that this mobilization calculus, however, is not strictly based on material interest. Rather, it is based on how an individual interprets their situation based on the plausibility of their cost and benefit calculus occurring, the stakes involved if they ignore the situation, and whether they can find some external validity for their interpretation of a situation. I term this as my first mobilization pathway. However, in addition to the first pathway, a small portion of my research subjects (6 out of 20) did get pulled towards a radical entity through factors at the meso level (such as networks of online or offline friends and acquaintances) as discussed in the previous research. Hence while my main argument challenges the finding related to the centrality of the role of networks in mobilizing people towards radical entities, I do concede that they are still important in certain cases. I term this as my second mobilization pathway. Through the discovery of these two pathways, this dissertation furthers the study of mobilization toward radical groups. Moreover, one of the central contributions is empirical as it is based on collecting primary source data from hard-to-reach populations. The dissertation establishes that while access issues are still present while studying radical entities, they are certainly not impossible to overcome. Finally, the dissertation makes policy-relevant recommendations that can help practitioners mitigate the conditions that are more likely to create mobilizing pathways for individuals.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".