Malicious Enclaves: Racism, Hate, and Violence in Social Media Use of Right Wing Extremists in Canada
Bibliographic record
Abstract
Canadian right-wing extremism is now recognized as a serious problem and their use of social media to circulate hate, violence, and racism is a growing concern.However, not until circa 2016 did Canadian academics, police agencies, and intelligence agencies view the threat of right-wing extremism as a national security issue within Canada.My dissertation examines the usage of social media by "right-wing extremist" (RWE) groups in Canada to contribute to the collective knowledge pool of Canadian right-wing extremism.It aims to identify hate as an affective form of communication regularly utilized by right-wing extremists through their usages of social media.This dissertation draws upon conceptual resources in media and communication studies to develop an analytical framework to examine how four Canadian RWE Facebook pages, Pegida Canada, Act for Canada, Canada Defence League, and Canada Three Percenters, created a digital space to justify and circulate racism, hate, and violence in social media.I use social network analysis, news event analysis, and frame shift analysis to unpack the digital space that leads to what I label as "malicious enclaves": a facade of a politically oriented group that establishes a veil of legitimacy to facilitate hateful and violence-endorsing views primarily on digital social media.The social network analysis generates an understanding of how malicious enclaves are connected and interacted with various Facebook pages, the news event analysis provides a glimpse at a two-day data inclusion window to ascertain how the malicious enclaves respond to a pre-defined news event, and finally, the frame shift analysis examines how the malicious enclaves collectively switch their frames to respond to a novel news event.My dissertation demonstrates how the online circulation of grievances around victimization has positioned racism and violence toward hated others, especially Muslims, as an RWE's defensive response necessary to protect the nation.My research into malicious enclaves unravel an intimate Dr. Arne Kislenko, you had enough grit to make a disinterested BA student an aspiring academic… thank you for opening my eyes to the world.Dr. Stephen Muzzatti, your time and dedication to my studies allowed me to formulate multiple studies that transformed my academic journey and ultimately shaped all that I study today.Dr. Sandra Robinson, your kindness, patience, and willingness to teach me all sorts of amazing tools made possible success I have found in and outside of academia.Dr. Jennifer Evans, your enthusiasm, passion, and constant willingness to guide and support my ideas made me a competent independent researcher
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 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".