A Case Study Examining One School's Trek Towards More Inclusive Practice
Bibliographic record
Abstract
The purpose of this chapter is to discuss an in-depth case study involving one school's mission to increase inclusive practices through the utilization of Universal Design for Learning paired with evidence-based practices. Specifically, a K-9 private school in Canada approached a research team about utilizing a measurement tool to measure the inclusion of students with autism spectrum disorders in inclusive classrooms. After much discussion, the researchers and team agreed upon a year-long course of action to improve services for all students within the school, including those with ASD. A course of intervention was outlined that included pieces such as a needs assessment, training coaches, multiple observation assessments, intermittent coaching, and scheduled professional development. The purpose of this chapter will be to discuss the overall plan designed as well as lessons learned along the way.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".