Transitioning from Segregation to Inclusion: An Effective and Sustainable Model to Promote Inclusion, through Internal Staffing Adjustments, and Role Redefinition
Bibliographic record
Abstract
Abstract This work explores the effectiveness of an innovative inclusion model that is based on the development and operationalization of the inclusion coach (IC) role in one school district (in Ontario, generally referred to as a ‘board’). This model has implications for school systems that desire a change in practice but may perceive challenges to this change in their local capacity. In this model, internal school district funding and existing structures were reallocated to convert teaching positions into IC positions. This staffing change was designed to support the desegregation of stand-alone special education classes at the elementary and secondary levels within that school district. While significantly decreasing the number of segregated settings, the intervention was not without its challenges. Challenges and successes will be examined through the perspectives of school principals, ICs and classroom teachers. This school district created an effective and sustainable model to promote inclusion, through internal staffing adjustments, and role redefinition. Utilizing a shared focus and support for staff, this school district was successfully able to transition beliefs and practices from segregated special education to full inclusion for students with special education needs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".