Secularism and securitisation: the imaginary threat of religious minorities in Canadian public spaces
Bibliographic record
Abstract
From the assumed physical threat of a ceremonial Kirpan in an elementary school carried by a Sikh child, to the fictional possibility of rich, Arab, Muslim University students utilising their implicitly understood patriarchal power to subjugate all women from access to common swimming pools, Canada has become increasingly replete with examples of using religious minorities as a danger to secure public spaces for societies most privileged. Since 9/11, this has become a far too common public discourse on maintaining close surveillance, scrutiny and regulations for those religious and racialised Canadian minorities associated with the ‘war on terror’. Promoting public spaces, especially public-school spaces, as ‘secular’ has become the argument of supposed non-bias in ensuring safety and equality for the wider population, all the while leaving many of those used as an example of threat to wonder if the ultimate intent is to preserve white, Christian (and Christian cultural) privilege. This article proposes to examine cases since 9/11 that have problematised racialised groups associated with the terrorism in public schooling to the benefit of maintaining ‘Old Stock’ status quo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.029 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".