Talking about violent extremism: Experiences of Canadian secondary school teachers in four metropolitan areas
Bibliographic record
Abstract
This study explores the perspectives and experiences of Canadian secondary school teachers around violent extremism through semi-structured interviews and focus group discussions with 30 (n=30) teachers from Montreal, Toronto, Calgary, and Vancouver. The findings reveal unanimous eagerness among participants to engage with controversial subjects, yet almost all of them exhibit discomfort in addressing violent extremism, primarily due to perceived deficiencies in their expertise and training in this area. Some teachers show reluctance to address these topics to avoid excluding or marginalizing specific student groups, notably Muslims. Interestingly, a minority of teachers suggest that white students are immune to radicalization. They also expose unconscious biases concerning radicalization among religious minority students, especially Muslims, reflecting dominant discourses around radicalization and Islam. Moreover, there exists dissent regarding the necessity of addressing radicalization in schools that seemingly lack youth radicalization, mirroring a reactive discourse in preventing/countering violent extremism (P/CVE). Alarmingly, some participants report Islamophobia among their colleagues, highlighting an urgent issue that needs attention. Drawing from these insights, the study advocates for comprehensive teacher training on violent extremism and emphasizes the importance of collaboration between schools, parents, and local education ministries. It also criticizes Canada’s National Strategy on Countering Radicalization to Violence for its shortcomings and calls for a more robust and inclusive approach to P/CVE. Ultimately, the study underscores the need to integrate an ethic of care into educational practices, fostering an inclusive environment where all students feel valued and supported.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.042 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".