Collaborating for Inclusion: The Intersecting Roles of Teachers, Teacher Education, and School Leaders in Translating Research into Practice
Bibliographic record
Abstract
Despite empirical research pointing toward the positive impact of an inclusive instructional approach and practices on all students’ learning and social participation, educators and schools lag in adopting these approaches and strategies. For the purpose of knowledge mobilization, it is important to examine the factors that influence this research-to-practice gap. With this aim, we first outline the significant role of teachers and teacher education in implementing inclusive practices. We then synthesize findings from previous literature identifying both individual and contextual, system-level influences that impede the implementation of evidence-based inclusive practices by teachers. We emphasize the prominent role of school leaders in removing some of these barriers by supporting teachers and collaborating with key stakeholders. Further research is needed to explore the complex, interrelated factors that foster collaboration among school leaders, teachers, and teacher education programs in order to advance the development of truly inclusive education systems.
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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 | 0.257 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.017 | 0.043 |
| Scholarly communication | 0.040 | 0.044 |
| Open science | 0.004 | 0.045 |
| Research integrity | 0.008 | 0.010 |
| 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".