Beyond the binary: rethinking teachers’ understandings of and engagement with inclusion
Bibliographic record
Abstract
This article presents research into Canadian elementary and secondary teachers’ understandings of inclusion. The research investigates how a sample of 120 teachers in the southern part of Ontario defined inclusion, and the extent to which they believed an inclusive classroom is an effective way to teach all students. The article draws upon literature into how inclusion is currently defined followed by research into the politics of diversity in inclusive education; the latter signals the socio-political aporia which attends many understandings of inclusion. The study employs Nancy Fraser’s conception of justice as requiring redistribution, recognition, and representation; Fraser’s approach also demands attention to issues of recognition as intimately connected with concerns about social status. The findings reveal teachers’ relative lack of attention to issues of resourcing, but considerable emphasis upon issues of representation. While issues of recognition are largely valued, there is a tendency to reify categories of student identity, rather than challenging concerns about the lack of social status attending such foci. The research reveals a push ‘beyond the binary’ of considering teachers’ practices as either inclusive or exclusive, and how teachers’ engagement with resource provision, recognition of learners, and representation of student needs exists along contingent and intersecting spectra.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.030 | 0.081 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| 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".