Bibliographic record
Abstract
In a post-9/11 sea of social and political discord, one state stands apart. As an increasingly powerful anti-Islamic social movement rises in the West, Canada alone remains a viable multicultural state. Employing survey and statistical data as well as a series of interviews conducted with religious leaders and policy officials, Protecting Multiculturalism explores public safety and security concerns, while pointing out the successes, pitfalls, and sometimes countervailing effects of government measures on Muslims in Canada. Engaging with debates surrounding the cultural accommodation of diverse communities, John McCoy focuses on two inter-related themes at the heart of the crisis of multiculturalism: social integration and national security. Even in Canada, McCoy argues, Muslims can face acute xenophobia and racism, problematic national security practices, inimical politicians, and other troubling warning signs. Yet, despite these challenges, these diverse communities continue to display remarkable resilience. An open-minded and substantive reflection on the day-to-day realities for Muslim communities, Protecting Multiculturalism seeks a way forward for the Canadian multicultural experiment - a future that is marked by dignity and diversity in an increasingly fraught era.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".