Responses to and interpretation of anti-Muslim racism in Canada : a community perspective
Bibliographic record
Abstract
Against the backdrop of rising levels of anti-“Muslim” racism (aka Islamophobia) in Canada, coupled with the nation-state’s targeting and surveillance of these communities, my dissertation sets out to interpret the responses to this racism by the affected communities themselves. In this study, I employ qualitative methodology within a critical race theoretical framework informed by indigenous and post-colonial theory. After inviting participation from self-identified Muslim and Arab community organizations, whether outwardly responding to racism or not, over a one year period (2011-2012), I interviewed eleven diverse organizations, all of which are working in various capabilities and focus on community capacity building – including in the sectors of professional mentorship and networking, activities such as multi/inter-faith programming, social services, and advocacy for their communities. I asked participants to share their narratives and views on a wide array of questions: their assessment of the situation of their communities and constituencies in Canada, their experiences with “community government,” and their assessment of the “good Muslim/bad Muslim” nexus. I classify data I gathered into a heuristic of three types of responses: direct, status and native informant, and argue that although most of them fall into the range of status, it is direct responses – ones that commence and attend to racial injustice – that can have the most positive impact in terms of overall responses to systemic anti-Muslim racism.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.056 | 0.027 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".