An institutional analysis of the Ontario Enhanced Teacher Education Program with a Focus on Inclusive Education.
Bibliographic record
Abstract
This study aims to examine the enactment of the Ontario Enhanced Teacher Education Program with a focus on inclusive education. Specifically, the study seeks to understand the institutional practices of policy actors including teacher educators, faculty, and teacher education administrators towards preparing teachers for inclusive classrooms in one Ontario faculty of education. The theoretical framework draws from the theory of New Institutionalism (NI) and the notion of ‘policy enactment’. The NI theory emphasizes the ways policy actors perceive and enact policies in institutional settings. Data collection includes semi-structured interviews with 10 policy actors in the research site and policy documents related to teacher education and inclusive education in Ontario. Data will also be collected from prospective teachers’ artifacts, faculty website, and researcher’s reflections after each interview. A qualitative, exploratory, single case study will be used to comprehend how the enhanced teacher education program is being translated into practices related to teaching, practical experiences of prospective teachers, and program development. The significance of the study lies in addressing a new analysis of how an institutional change informs the practices of policy actors towards advancing the preparation of Ontario teachers for the 21 st century’s inclusive classroom.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".