Canadian School Programs for Students with Emotional/Behavioral Disorders: An Updated Look
Bibliographic record
Abstract
Canada is a country composed of 10 provinces and three territories with very limited federal control over the public K-12 education system. This lack of consistent direction has led to a range of diverse educational policies for educating students with emotional/behavioral disorders (E/ BD). This article investigates the current state of policies related to the education of students with E/ BD. A mail-out survey was submitted to provincial/territorial departments of education and directed to individuals responsible for E/BD programs, composed of questions regarding their government's policies and regulations concerning the education of students with E/BD. A 100% rate of return was achieved. This study found discrepancies across Canada in most areas surveyed, including differences in definitions, diagnosis, identification, prevalence, eligibility, placement, services, administration, funding, and teacher training. Trends toward noncategorization and inclusion exist. Conclusions include the need to balance an inclusive model with consistent identification and service provision, to consider the potential for difficulties in regular classroom placement, to provide population-specific teacher education, and a call for national consistency in the provision of effective identification and programs across Canada for students with E/BD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".