“Just make them feel welcomed”: examining newcomer ESL students’ intersectional racism in Canadian schools
Bibliographic record
Abstract
This study examines the role of multilingual and multimodal literacy engagement in supporting the integration of newcomer students, emphasizing digital technologies and diverse multimodal texts. Utilizing a workshop methodology, the interactive and participatory research process empowered participants to use their home language, culture, and religion to influence their English language learning and social integration while contributing their perspectives to help shape the study’s discussions and outcomes. These newcomer and racially diverse students effectively (re)negotiated their linguistic, cultural, racial, and religious identities as it explored participants’ experiences and delved into conversations around how racialization is constructed and understood among stakeholders, focusing on students’ lived experiences of marginalization, identity construction, negotiation, and resistance, and suggesting potential avenues for change. The study identified challenges such as linguistic and cultural barriers to social integration and discrimination based on stereotypes and misconceptions regarding racial and religious identities and affiliation. It also unraveled the complexities posed by cultural disparities, particularly those involving religious practices, dress, and belief systems. These insights underscore the need for more inclusive school environments that acknowledge diversity, promote racial and religious tolerance, and address the intersectional challenges faced by racialized newcomer students. Respecting boundaries, fostering cultural understanding, and creating an environment that embraces differences are crucial steps toward mitigating intersectional racism experienced by newcomer ESL students.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.041 | 0.013 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".