A work in progress: inclusion for students with developmental disabilities from the perspectives of principals and teachers
Bibliographic record
Abstract
Legislatively and practically, school districts around the world have transitioned to more inclusive service delivery for students with disabilities. Despite consistent and meaningful changes across the spectrum of disability, students with Developmental Disabilities (DD) remain segregated in self-contained classrooms at a high rate. Within a Canadian context numbers vary: some provinces are fully inclusive and others continue to segregate students with disabilities. In Canada’s most populous province, Ontario, the number of students with Developmental Disabilities (DD) in segregated settings remains consistently high (Bennett, S., D. Dworet, T. Gallagher, and M. Somma. 2019. Special Education in Ontario Schools. 8th ed. St. David's ON: Highland Press). Despite these concerning numbers, individual school districts are trying to shift practices to more fully inclusive service delivery. This paper examines one Ontario school district that transitioned to full inclusive education for students with disabilities. Utilising 10 principal and 21 teacher interviews, this paper examines the perceptions of these stakeholders as they reflect on their transition to inclusion and their experiences. Implications discuss the participants’ perceived successes and challenges, and recommendations provide insights to assist school districts in shifting embedded practices of segregation towards full inclusion for all students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.014 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".