A Path Forward: Empowering Teachers to Implement More Inclusive Special Education Frameworks in Ontario
Bibliographic record
Abstract
This qualitative study employs a social-critical constructivist framework to investigate current Ontario teachers’ knowledge and opinions of inclusive education (IE) initiatives for special education in Ontario. Twenty-two participants were interviewed to gather data on their current understandings of IE policies and initiatives, as well as how they might re-define their current roles to actualize these IE policies across their broader school communities. Current IE empirical research that gathers similar attitudinal data represents teachers as uninformed, uncritical, and powerless; any recommendations that these studies present come from the researchers rather than the participants. Research findings also demonstrate that while teachers philosophically agree with IE, they question the extent to which they can successfully implement IE due to time constraints, limited resources, and inconsistent administrative support. This study’s constructivist framework demonstrates that participants have carefully reflected on IE within their instructional contexts. Participants are determined, engaged, knowledgeable, and conscientious professionals who are currently mobilizing to take on future IE implementation initiatives. Their critical perspectives helped to form the basis of this study’s recommendations. This study’s detailed policy analysis of Ontario Ministry of Education equity documents also reveals that these documents do not include teachers’ voices. This study specifically examines IE through a special education lens in order to achieve a deeper understanding of the knowledge gaps that significantly hamper teachers' abilities to successfully implement IE initiatives. To address these professional knowledge gaps, this study makes four significant recommendations. First, teachers should engage in professional learning that specifically focuses on Ontario IE policies. Second, job-embedded professional learning should serve as the departure point for all future IE implementation initiatives in Ontario. The third recommendation is that teachers must be given direct consultative opportunities to share their visions for future IE initiatives. Fourth, Ontario schools must move away from their traditional bell schedules to more flexible timetabling and staffing allocations. These recommendations are also significant because they are informed by participants’ lived experiences and have been proven successful in comparable North American education systems.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".