Transitioning towards inclusion: a triangulated view of the role of educational assistants
Bibliographic record
Abstract
Abstract The current work employs the use of multiple lenses to illuminate the integral role of the Educational Assistant (EA) in a Canadian school district’s transition from a segregated to inclusive service delivery model for students with special needs. Often compatible, but also distinctive viewpoints and understandings shape the role of EA in this journey towards inclusive practice in the context of a district that deployed Inclusion Coaches to support educators over the course of five years. Through deductive analysis, qualitative data (interviews, focus groups, blog‐style reflections) were analysed from EAs (n = 6), elementary and secondary school teachers (n = 31), and Inclusion Coaches (n = 13). Findings indicated that the voices of EAs, teachers, and Inclusion Coaches all align on three main themes: necessity of collaboration among educators, coordinated and dedicated time for programming and redefining relationships within the inclusive model. During the transition from segregated classrooms to full inclusion, it is imperative that the role of the Educational Assistant (EA) is understood by administrators, teachers and the EAs themselves. With a more clearly delineated and mutually understood role, EAs and educators can develop collaborative relationships, working towards incorporating differentiation and supporting all students in a diverse learning and social community.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".