The Paradox of Partial Day Schooling: Exclusion in the Era of Inclusive Education
Bibliographic record
Abstract
Abstract Background: Despite legal mandates supporting inclusive education in Canada, students with disabilities continue to experience systemic exclusion through partial-day schooling. This practice, often implemented due to limited staffing, inadequate training, and insufficient resources, disrupts learning, social interaction and emotional development while undermining students’ rights to full participation. Aims: To examine partial-day schooling as a harmful exclusionary practice, explore its systemic causes and consequences and highlight the roles of families, advocacy organisations and educational leadership in advancing full-day inclusive education. Methods: Narrative analysis drawing on policy investigations, parent testimonies, advocacy reports and oversight body findings from New Brunswick and British Columbia to contextualise partial-day schooling within broader systemic inequities. Results: Partial-day schooling causes academic disruption, emotional distress, institutional marginalisation and family burdens, reflecting systemic policy failures rather than isolated cases. Advocacy organisations help bridge policy and practice gaps, but sustainable reform requires leadership action. Conclusion: Partial-day schooling contradicts the principles of inclusive education, representing a systemic denial of rights. Eliminating this practice requires adequate resourcing, cultural change, accountability and full implementation of Article 24 of the CRPD.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.055 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".