Moving Toward Inclusion: Inclusion Coaches' Reflections and Discussions in Supporting Educators in Practice
Bibliographic record
Abstract
When school systems and administrations provide educators with opportunities to engage in transformative learning through reflective practice and provide opportunities to challenge their beliefs, educator pedagogy for inclusive education can be enhanced (Evans, 1997; Pyhältö et al., 2012; Richardson, 1998). Our research examined the experiences of 11 inclusion coaches while they provided support and built capacity for 38 educators during a change in special education service delivery, seeking insight into the effectiveness of this coaching model. Coaches’ experiences were shared during semi-focused group discussions and via an online blog. Qualitative analysis revealed coaches’ roles in this context were influenced by their personal expectations, personal growth, support for one another, and support for respective educators. The findings from this research are pivotal for pedagogy and teaching philosophy in inclusion.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".