ESL infusion in pre-service teacher education: a vehicle for exploring the beliefs and practices of teacher educators
Bibliographic record
Abstract
Key findings suggest that, despite the constructivist and critical approaches to teacher education reflected in much of the data, institutional and structural constraints accompanying a nine-month B. Ed. program delivered to 1300 candidates limit the success of adequately addressing diversity and equity-related initiatives such as ESL Infusion. Additional support should be provided for teacher educators in promoting inclusive practices through revised policies and models of program delivery, recruitment of both teacher candidates and instructors who demonstrate anti-discriminatory stances and practices, and formal education for teacher educators. This thesis analyzes the extent to which three different teacher educators at the Ontario Institute for Studies in Education of the University of Toronto (OISE/UT), operating within the constraints of their professional circumstances and the scope of their beliefs, promote ESL-inclusive teaching strategies and issues in their pre-service courses as part of an institution-wide ESL Infusion Initiative. Examining ESL infusion in teacher preparation is particularly relevant at a time when increasing cultural and linguistic diversity in Ontario schools coincides with reduction in resources and specialist support for ESL learners. Using critical ethnographic research methods, I use the lens of ESL infusion to consider the beliefs and practices of teacher educators as they relate to ESL-inclusive pedagogy and concepts of effective teacher preparation more broadly.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".