Critical Language Awareness in Motion: Disrupting Dominant Spaces through CELLWs
Bibliographic record
Abstract
One of the primary challenges for multilingual students in Canada is to learn English and integrate into mainstream society. Within this process, students’ intersectional identities (the intersection of race, language, culture, and religion) influence their L2 learning while creating overlapping and interdependent systems of discrimination and disadvantage. This article explores “multilingual awareness” and its interplay with “spatial awareness” among high school newcomers in Canada. In particular, it focuses on how they employ linguistic, cultural, racial, and religious backgrounds in navigating English language learning spaces. The study involved 13 students from grades 10 to 12, all of whom participated in a series of Critically Engaged Language and Literacy Workshops (CELLWs). These workshops employed arts-based research methods and walking methodology that facilitated discussions about students’ experiences and provided opportunities to challenge them in the context of their multilayered identities. The question guiding this study was, “How does newcomer high school students’ spatial awareness, as explored through CELLWs, contribute to the disruption of dominant English language spaces?” Findings highlight the critical role of spatial awareness in fostering connections to multilingual identities and revealing systemic discriminations tied to these identities. Overall, the newcomer students’ narratives underscored the significant role of community and school interactions in shaping their sense of identity and belongingness, as well as discrimination and disadvantages that were intrinsically related to their intersectional identities.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".