From Stigma to Strength: A Case of ESL Program Transformation in a Greater Vancouver High School
Bibliographic record
Abstract
The rapid increase of Asian immigrant students in Canadian classrooms demands more systematic and increased language support to ensure all English language learners (ELLs) achieve success in school. However, research has found mixed results on the usefulness of current English as a Second Language (ESL) support programs and a growing dissatisfaction among students and parents about ESL, suggesting further investigation is needed to improve the provision of ESL in the schools. This paper details how one school and one ESL teacher responded to the needs of newly arrived Asian (i.e., Chinese) ELL students by documenting the school’s and teacher’s journey in revamping the pull-out ESL program into a culturally responsive English for Academic Purposes (EAP) program with a focus on immersion, community engagement, and a pedagogy of cultural reciprocity. The case has important implications for redesigning current ESL programs in the context of changing immigration.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.049 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| 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".