Authentic Inclusion in Two Secondary Schools: "It’s the Full Meal Deal. It’s Not Just in the Class. It’s Everywhere."
Bibliographic record
Abstract
Inclusive educational practices vary across Canada, and perhaps most especially in secondary schools. Researchers use the term authentic inclusion to describe exemplary inclusive educational institutions. Using an appreciative inquiry framework, two such high schools were identified and profiled within the Canadian province of Saskatchewan. Students with and without disabilities, parents and/or guardians, teachers, educational assistants, and other school-based personnel were interviewed using semi- structured protocols. Data were analyzed and two main interrelated themes emerged; the first, authentic inclusion: “the full meal deal—it’s everywhere”; and the second, inclusive pedagogies. Several sub-themes provide greater detail, namely: a) a broad and infused inclusive vision, (b) leadership: implementing the vision, (c) pushing all students beyond comfort zones, (d) no to the new exclusion, and lastly, (d) rejection of false dichotomies: specialized care vs. social inclusion. In the final section, the notion of hope is taken up, as it hearkens back to the appreciative methodology, and more generally, to the promise of authentic inclusive education. We explore the notion of hope-filled schools, and students’ hopes for the future. Hope may be a critical element in the practice of authentic inclusion for students with disabilities.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.022 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".