School leadership and inclusive education in Canada: Considerations for comparative and international research
Bibliographic record
Abstract
The central question that the papers in this symposium respond to is, “How might the intersection of fields of research in Canada, including school leadership, special education, and racialized youth, inform the development of more inclusive forms of education globally?” Four papers will be presented based on research findings that address inequalities and educational opportunities for marginalized youth in Canada. The papers provide interdisciplinary, collaborative, and mixed-methods approaches to respond to the overarching question. The breadth of topics in the papers include diverse topics (disability, racialized youth, school leadership) and jurisdictions (English and French speaking). In so doing, the symposium enables an opportunity to consider the argument of Ainscow and Sandill (2010) that, “The issue of how to build more inclusive forms of education is arguably the biggest challenge facing school systems throughout the world” (p. 401). The discussant will provide a synthesis of the papers and suggest considerations for international and comparative research on the intersectionalities of school leadership and inclusion.
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 | 0.059 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.015 | 0.036 |
| Science and technology studies | 0.039 | 0.023 |
| Scholarly communication | 0.035 | 0.015 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".