A New-Institutional Analysis of Inclusion Policy Enactment in Teacher Education: A Case from Ontario
Bibliographic record
Abstract
This qualitative single case study aimed to examine the logics of one teacher education program towards preparing pre-service teachers for inclusive teaching from the perspectives of the program’s coordinators. In particular, the study aimed to understand the practices of these coordinators and how these practices are influenced by inclusive education and teacher education policies. This examination would reveal how education policies are enacted in this particular case. New-Institutionalism (NI) theory (DiMaggio & Powell, 1991) constituted the theoretical framework that guided the methodology as well as the analysis of the findings. The study revealed that the coordinators’ understanding and practices around the existing inclusion and teacher education policies emerge from their own experiences in this particular program, intermingled with their beliefs about how inclusion should be enacted in teacher education and schools. Key findings included coordinators developing inclusive mindsets among pre-service teachers, negotiating their logics towards inclusion through modeling inclusive teaching practices in the university classroom, and engaging them in critical discussions around inclusion policy practice in schools, and coordinators calling for a curriculum policy change. Recommendations for future teacher education programming in response to the evolving inclusive education are offered.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".