Self-determination and inclusion: The role of Canadian principals in catalysing inclusive-positive practices
Bibliographic record
Abstract
While the placement of students with complex learning needs in Canadian classrooms may be mandated in policy, the development of inclusive-positive practices requires direct, and at times delicate, support and encouragement from principal teams. Without genuine engagement and buy-in from teachers and school staff, students in inclusive classrooms may not find meaningful opportunities in those spaces. Often, it falls on principals to be leaders of attitudes and practices so that inclusive school communities can thrive. Fortunately, the framework of self-determination theory provides a path by which principals can catalyse attitudinal changes (autonomy), best practices (competences), and enriched community relationships (relatedness). This study includes the questionnaire responses of 275 principals (and vice principals) from six provinces in Canada. Of those 275 participants, 46 principals also participated in interviews. The findings of this study suggest that affecting attitudinal changes requires specific and comprehensive practices. Principals also need to model inclusive-positive attitudes in their behaviour and practice. Finally, the careful curation and maintenance of relationships is a necessary for the well-being of the school communities. Implications for principal practice and school well-being are included.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".