Promoting the Everyday: Pro-Sharia Advocacy and Public Relations in Ontario, Canada’s “Sharia Debate”
Bibliographic record
Abstract
Why, in the midst of public debates related to religion, are unrepresentative orthodox perspectives often positioned as illustrative of a religious tradition? How can more representative voices be encouraged? Political theorist Anne Phillips (2007) suggests that facilitating multi-voiced individual engagements effectively dismantles the monopolies of the most conservative that tend to privilege maleness. In this paper, with reference to the 2003–2005 faith-based arbitration debate in Ontario, Canada, I show how, in practice, Phillips’ approach is unwieldy and does not work well in a sound-bite-necessitating culture. Instead, I argue that the “Sharia Debate” served as a catalyst for mainstream conservative Muslim groups in Ontario to develop public relations apparatuses that better facilitate the perspectives of everyday religious conservatives in the public sphere.
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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 | 0.002 |
| Science and technology studies | 0.056 | 0.024 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".