Bibliographic record
Abstract
This paper explores the practices of certain high-profile Canadian Muslims who call themselves “progressive” or “moderate” as an example of attempts to increase one’s claims to national belonging through a reification of tropes that designate many Muslims as fanatical, scary and a threat to the Canadian nation. Through tracing the different understandings of “Muslim” and “Canadian” identities and an examination of articles printed in The National Post, this paper argues that this accumulation occurs in three main ways, with portrayals of the “good” Muslim as a patriotic Canadian, as an object of threat from other Muslims and as a protector of oppressed Muslim women. However, in a context marked by rampant Islamophobia throughout Canadian society, these nationalist practices may do more to produce further racialisation of and violence towards those that they positioned as “bad” Muslims than to ensure any lasting claims to national belonging for those who assert themselves to be representative of the “good” Muslims.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.062 | 0.046 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".