Integrating Islamic thought as intercultural praxis into secondary schools curriculum in Canada: an Islamic school in Saskatchewan
Bibliographic record
Abstract
As the battle continues over what to teach and which writers and ideas should be included in Canada’s English Language Arts secondary school curriculum, this article examines how to infuse the ELA curriculum in Canada with culturally relevant praxis and lexicon for Muslim high school students. The aim is not to confine Muslim education to storytelling, a fictionalisation of religion, or a theme-based integration of sociohistorical narratives. Instead, we ask: How can teaching ELA in Canadian schools contribute to a Muslim voice in education? How can teachers make Islamic thought relevant to Muslim youth’s daily intercultural interactions in diaspora? How can core curriculum subjects be taught beyond the repeated patterns of Islamic narratives and Qur’anic themes? The article explores how to implement Islamic concepts and practices such as Nafs (soul) and Maqasid (intentions) as pedagogical strategies in an Islamic high school in Saskatchewan to address relevant Muslim diasporic conditions. The paper is not based on data collection methods. It is research through teacher experiences teaching a secular curriculum in Muslim-majority schools. In contrast to Islamic education, which is often Qur’anic-based, this article argues that Muslim education refers to applying Islamic thought to the learning experiences of Muslims in secondary education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".