Beyond the Rhetoric: Moving from Exclusion, Reaching for Inclusion in Canadian Schools
Bibliographic record
Abstract
This article is informed by the findings of a three-year research study in Ontario schools in order to understand the factors and forces that make for students' engagement and disengagement in schools. Although research has sought to understand the processes that contribute to some students feeling a sense of marginality and disconnectedness in their schools, we have also paid attention to exemplary practices of inclusive schooling in educational settings. Specifically, in this article we examine educational practices that engender exclusion or inclusion, particularly of racially marginalized students in Euro-American or Canadian contexts. We develop an analysis that uncovers the connection between "inclusionary and exclusionary" practices of schooling. "Inclusivity" moves beyond mere classroom presence of minorities or superficial attempts at multiculturalism: students may feel disempowered and therefore excluded as far as actual classroom practices are concerned (e.g., teaching, sharing knowledge). Moving from exclusion means identifying students' own narrative accounts of marginality and subordination that result in feeling left out. Reading for inclusion means interrogating strategies initiated by schools, students, educators, parents, and local communities to counteract the marginalization of disadvantaged and racial minority youths. Our aim in this article is to use available research information to encourage the wider application of effective inclusive practices to improve learning outcomes for all youth.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.087 | 0.026 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".