Representations of Inclusion: How Pre-service Teachers Understand and Apply Inclusion Across Situations
Bibliographic record
Abstract
As education marches toward inclusive practices, it is clear not everyone perceives inclusion in the same ways. This article unpacks pre-service teachers’ perspectives toward inclusion. Using a design-based approach, enrolled in a Canadian bachelor of education program created drawings about inclusion. Through the lens of figured worlds and visual analysis, these drawings were compared to pre-service teachers’ other course work and practicum placements to better understand their perspectives on inclusion in different contexts. The findings show differences between the ideals pre-service teachers conveyed through drawings and their approaches to pragmatic tasks, such as creating an individualized education or inclusive lesson plan. Furthermore, there was a considerable range of sophistication in pre-service teachers’ ideas of inclusion, with some pre-service teachers focusing on accessible materials while other pre-service teachers addressed more holistic notions of inclusive environments and systems. Making connections between pre-service teachers’ perspectives and their teacher training is critical for pre-service teachers to develop robust understandings of inclusion and a greater awareness of how their perspectives shape teaching practices.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".