Inclusive learning spaces in Australian schools: an exploration of stakeholder participation, perspectives and priorities
Bibliographic record
Abstract
Most Australian children with disability who attend school are enrolled in mainstream schools, yet insights into their spatial experiences of school environments are limited. This research explored the experiences of students with disability in various school settings, focusing on the physical enablers and barriers to their participation (C. Imms et al., Citation2017).Data were collected via 18 focus groups with stakeholder groups that included students and young adults with disability (18+ years), parents, disability representative organisations, special school principals and educators. Focus groups were analysed by a team with backgrounds in education, architecture, and health/disability, using reflexive thematic analysis (Braun & Clarke, Citation2006).Analysis revealed three themes: 1) genuine commitment to inclusive education, 2) spaces that are available, accessible and adaptable, offering variety and choice, and 3) recognising diversity and the need for responsive settings for teaching and learning.The findings highlighted the need for spaces that can adapt to individual learners, emphasising the importance of forming partnerships with key stakeholders. Co-design processes involving people with disabilities were seen as vital for identifying barriers and enablers to students' meaningful participation. Teacher professional development focused on supporting students' spatial and environmental needs was also recognised as essential for inclusive education.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 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".