Translanguaging and language maintenance among Arab students: immigrants and refugees
Bibliographic record
Abstract
As Canada experiences an influx of immigrants and refugees, K-12 classrooms are becoming increasingly multilingual. Providing educational materials and resources in multiple languages does not guarantee that students new to Canada will become bilingual in their native languages (L1) and Canada’s official language (L2). Therefore, it is necessary to re-evaluate best practices from the late 1970s and early 1980s, when using the native language in language-teaching classrooms was deemed unacceptable. This study examines English/Arabic translanguaging and language maintenance practices by Arab immigrant students and those from refugee backgrounds. Using positioning theory, this research focuses on participants’ development of positions and translingual identities, and on analyzing the distribution of rights, duties, and obligations through conversations and narratives. This research further examines how Arab students employ their linguistic abilities to acquire knowledge, enhance comprehension, and foster global identities. Using English, Arabic, and translanguaging in different contexts among immigrants and students from refugee backgrounds reveals similarities and differences influenced by their diverse experiences, cultural heritages, and social environments. These students need help communicating in their non-dominant language, and they often encounter stereotypes and misunderstandings regarding their linguistic proficiency. However, both groups recognize the immense value of bilingualism as it offers numerous advantages for personal, social, cognitive, and educational growth.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".