Improving Inclusive Practices in Secondary Schools: Moving from Specialist Support to Supporting Learning Communities
Bibliographic record
Abstract
Much of current inclusive education provision in Australian secondary schools relies on ‘specialist’ support from outside the classroom. Students with diverse abilities are placed within the regular classroom and additional specialist services such as therapy, counselling, psychological assessment and special education teaching are required to support their placement. This form of support often relies upon a diagnostic – prescriptive approach where the specialist assumes responsibility for and has a central role in shaping practices. In this paper, we raise issues regarding this model of specialist support in enacting effective inclusive practices in secondary schools. We suggest that one alternative is to shift the focus from the students who are different, to the community of learners in the school. This alternative approach uses collaborative and evidence-based practices to support inclusive ideals and grounds improvement efforts in changes in teachers’ knowledge as well as the cultural and organisational conditions of the school. The purpose of this paper is to prompt further discussion amongst professional community regarding the unique challenges and issues of inclusive practices in secondary schools.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".