A Macrosystem Contradiction: Examining Inclusive Education Supports, Training, and Structures
Bibliographic record
Abstract
The purpose of this dissertation is to exemplify special education supports, training, and structures as contributors to inclusive education for students with disabilities. Grounded in Bronfenbrenner’s bioecological model, this collective work evaluates various nuances of inclusive education across multiple ecological layers that systemically affect the developing child. The manuscript-style dissertation comprises three research articles. The first article presents a systematic literature review of procedural fidelity in peer-mediated interventions at the secondary level. Across the 28 included studies, peers demonstrated generally high procedural fidelity. However, critical appraisal revealed inconsistencies in how fidelity was defined, measured, and reported. Studies that required peers to achieve a mastery criterion during training reported significantly higher procedural fidelity outcomes. These findings challenge researchers to move beyond checklist-style fidelity assessments toward more rigorous, meaningful measures. The second article employs content analysis to examine the course offerings of Ontario’s pre-service teacher education programs. Results showed substantial variation in disability-focused coursework and a notable absence of content related to collaborative practice. This inconsistency highlights the urgent need for more coherent, mandated preparation for inclusive practice across Ontario pre-service teacher education programs. The third article uses an embedded mixed methods design to analyze special education plans from 54 Ontario school boards, linked to provincial sociodemographic data. Categorical analysis revealed misalignment between current reporting practices and outdated Ontario Ministry of Education guidelines. Exploratory factor analysis identified two dominant human resource structures: a traditional special education structure and a more inclusive structure. Further regression analyses demonstrated significant differences in HR allocation by school level, with greater exclusionary staffing practices in secondary schools. Results also showed relationships between sociodemographic variables and human resource allocations. This dissertation interrogates the fragmented implementation of inclusive education in Ontario public schools, which must be addressed systemically to acknowledge how multiple ecological layers interact to shape student experiences.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 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".