Experiences of South Asian Teachers Teaching South Asian Students in Greater Toronto Area Schools in 2016
Bibliographic record
Abstract
Since the population of ethnic minority students in the Greater Toronto Area (GTA) is increasing, new and ongoing research is required to meet the needs of these students. In response to the changing demographics, this research project investigated the experiences of South Asian teachers (SATs) working with South Asian students (SASs) in GTA classrooms. Semi-structured interviews were conducted with three South Asian school teachers from the GTA about their efforts to create an inclusive classroom for culturally, linguistically, and ethnically diverse students. These teachers used their own or their family’s experiences to create a supportive learning environment, which in turn reportedly allowed students to share and affirm their identities. The SATs reported that this strategy improved the literacy achievements and oral presentation skills of their students, including South Asian students. This study and related literature suggest that there may be a connection between students’ academic success and having a teacher who speaks their language of origin. Other study findings suggest that SATs’ relationships with South Asian parents can be both positive and negative. A significant finding in this study is that the SATs reported using their heritage languages and culturally relevant materials in their teaching. The study offers both immediate and long term recommendations for South Asian parents, settlement workers, resource teachers, school administrators, policy makers, and school boards.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".