Canadian Schools in Transition: Moving From Dual Education Systems to Inclusive Schools
Bibliographic record
Abstract
This paper provides a synthesis of educational reforms in Canadian schools over the past century to present times. The unique emphasis is to document the broad movements of change in both special and regular education. We begin our analy-sis with a detailed discussion on the many meanings of school restructuring and highlight the ongoing nature of school reform. Following a selective chronology of general and special education reform, we attempt to capture what appear to be the key features of school reform and progressive inclusion. The numerous obsta-cles to school reform are outlined and the evolving roles of those most centrally connected with the school culture—teachers, students, and parents—are re-viewed. Several conditions for successful change are presented and the adoption of a balance of interests, policies, principles, and practices is recommended along with a transformation from dual systems to a unified system of education for all students. Regular and special educators are the professionals who must make school transformation reflect excellence and equity in our Canadian schools, and all available resources and support need to be deployed to this end.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".