Entitlement, Victimhood, and Hate: A Digital Ethnography of the Canadian Right-Wing Social Media Landscape
Bibliographic record
Abstract
This dissertation is, at its core, an interrogation of white masculinity in Canada’s right-wing spaces. While my interlocutors spent a great deal of time discussing others, namely immigrants, globalist elites, and feminists, through their discourse, they revealed a lot more about themselves and their perceived victimhood (Berbrier, 2000). This victimhood is derived from what Hage (2000) refers to as the white nation fantasy, wherein white people believe they have the right to rule, control, and dominate in their countries. They are entitled to this by virtue of their whiteness and its perceived superiority, and thus feel justified in their harmful behaviour (Essed & Muhr, 2018). Yet, as I show throughout each chapter, that right is challenged time and time again by immigration, feminism, and racial justice, which triggers a sense of aggrieved entitlement (Manne, 2019) and backlash (Boyd, 2004; Braithwaite, 2004). Moreover, I demonstrate that this is not only a white fantasy, but rather a white male fantasy. While the white nation fantasy relies on white supremacy, the white male nation fantasy interweaves notions of male supremacism wherein not only are people of colour inferior, so too are women – including white ones who do not fall in line. I draw on bell hook’s conception of “white supremacist capitalist patriarchy” to show how their discourse, while explicitly racist and nativist (Schrag, 2010), upholds and is in turn upheld by both capitalism and patriarchy. Thus, while chapters on hockey, promiscuous women, and a “Sad Keanu” meme may seem disparate and disjointed, they all connect back to these notions of supremacism, entitlement, and ultimately victimhood.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.042 | 0.019 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".