Islamic Education and Multiculturalism: Engaging with the Canadian Experience
Bibliographic record
Abstract
Canada has a long history of immigration by diverse ethnic minority groups arriving in the hope of establishing economically successful – yet socially and culturally distinct – communities based on particular values and beliefs not necessarily shared by the ethnic majority. In recent years however the arrival of new immigrants whose values differ from the mainstream has intensified the multicultural debate, as the aspirations and needs of ideologically-motivated minorities feel current policies and institutions marginalize their values and beliefs (not dissimilar to that historically encountered by Canada‘s indigenous people). As a result of these social divergences, the secular state and orthodox religious groups often compete for the hearts and minds of children. Consequently as Muslim communities in Canada seek to protect their children and youth from perceived negative outside influences so Islamic schools have been established. Such schools face particular challenges in negotiating the tensions between their aspiration to preserve Islamic values and wider socio-political pressures to integrate into Canada‘s multicultural society as a whole. This article engages with this tension to uncover and explore the nature of Islamic education and its potentially contested relationship with Canada‘s multicultural ideals. In concluding it reflects on possible ways in which multicultural-Islamic education tensions might be ameliorated.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.083 | 0.023 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".