Championing Inclusive Education in Canada: Voices of Educators, Advocates, and Researchers
Bibliographic record
Abstract
Previously conducted research overwhelmingly supports inclusive education for all students, however inclusive education is not always provided in Canada. This project aims to understand the current state of inclusive education in Canada. Participants included in this study were 33 experts in inclusive education in Canada and can be categorized into three groups: researchers, advocates, and educators. Chats regarding each participant’s experiences with inclusive education were transcribed and thematic analysis was used. Six themes emerged: Family; Values and beliefs; Definition of Inclusive Education; Networking/Connecting; Information, policy and implementation; and School systems. Results demonstrated that there are some happenings in inclusive education that are working well and some that require improvement. The participants’ varied viewpoints allow for a comprehensive understanding of the current state of inclusive education in Canada, as well as important next steps. Such an understanding is critical to make further progression towards inclusive education.
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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.057 | 0.021 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".