Mapping Immigrant Children’s Ethnoracialized Identities in Canada: K–5 Muslim Students Share Stories with their Mothers
Bibliographic record
Abstract
This study investigates the schooling experiences of K–5 Muslim immigrant children to address the underexplored area of post-migration schooling within the Canadian context. Centered on the stories K–5 children share with their mothers, the study focuses on students’ identity formation, sense of belonging, and academic performance. Theoretically grounded in critical race theory and decolonial education as conceptual frameworks, the research explores the multi-dimensional experiences of immigrant children, moving beyond the monolithic narratives often enacted by dominant power structures. Utilizing a qualitative methodological approach, the study engages 10 Muslim-identifying mothers in semi-structured interviews, revealing insights about the role of mothers as knowledge holders and validating K–5 immigrant students’ schooling experiences. Findings indicate key themes including subtractive teacher practices, subversive allyship, racialization, marginalization, and the interplay of identity and religion. The study proposes targeted recommendations for school-based supports, teaching practice, and programs of teacher education to address post-migration schooling challenges.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| 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".