Brampton, Browntown, or Bramladesh? A Critique of the Hate Speech Used Against Racialized Spaces in Canada
Bibliographic record
Abstract
This study investigates the online hate narratives around South Asian citizens in Canada. The value of this research is that it challenges the perception of Canadian multiculturalism and by addressing the reality of multiculturalism, it reveals the racial hierarchical structure within ‘equal’ multicultural state like Canada. In this study we connect early 20th century history of recently arrived South Asian migrants to British Columbia to the literature of white nationalism and white fantasy, the myths of multiculturalism and the racist nature of online platforms to expose a shift towards the perspective and acceptance of South Asian people in Canada. Here, the study compiles 120 comments identified as hate speech and organized the comments into categories of general South Asian hate, specific Punjabi hate, specific Indian hate, commentary on social space, insult towards linguistic abilities and claims advocating for deportation from Canada and when relevant, the comments are categorized into subcategories of ‘Becoming India’, white replacement theory and loss of Canadian culture. The results of this coding showcase that the most prevalent narrative found in the YouTube comments were commentary on social space and more precisely commentary on white replacement theory. The implication of this dominant narrative is that it signals to a growing resistance to racialized spaces in Canada. This implication allows for the argument that online platforms such as YouTube should be viewed as an extension to the body of the dominant white nationalist structure in colonial society because these platforms are able to encourage and sustain a white fantasy and white power structures through the platforms structural racist nature that encourage the growth of hate narratives online.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.024 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".