Voices and Visions from Ethnoculturally Diverse Young People with Disabilities
Bibliographic record
Abstract
Many Canadian children from minority status groups experience long-term academic complexities, influencing their sense of school belonging and engagement. Research demonstrates children with intersecting differences of race, ethnicity, language, and disability, and those in their middle years (10–13 years old), undergo heightened academic challenges. Yet, what are children with disabilities’ personal schooling experiences, and how may these insights support inclusive learning, teaching, and sense of belonging? Within Toronto, one of the most diverse Canadian cities, this book explores the stories and experiences of six middle years children with intersecting differences of race, ethnicity, language, and disabilities (particularly autism). Through narrative and critical discourse analysis research methods the children’s views were accessed via a mosaic multi-method data collection approach, including their own photography, drawings, journal writings, imaginative story games, and interview texts. The children’s narratives illustrate their understandings of differences, learning, and inclusion. This book presents innovative insights highlighting the voices of children with disabilities as they navigate through complex issues of diversity and share how these impact their understandings and experiences of school inclusion and exclusion. The author advocates inviting the voices of children with intersecting differences into educational conversations and research processes, as they may adeptly advance areas of inclusion and diversity. .
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.045 | 0.029 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".