The Rhetoric and Reality of Leading the Inclusive School: Socio-Cultural Reflections on Lived Experiences
Bibliographic record
Abstract
This paper details a cross-cultural study of inclusive leadership practices within a basic education context in each of the following countries: Australia, Canada, and Colombia. Each school was selected after district educational leaders identified the school as being inclusive of students with diverse learning needs over an extended period of time. The researchers were particularly interested in the norms and assumptions that were evident within conversations because these were viewed as indicators of the nature of the embedded school culture within each context. School leaders and teachers were interviewed to determine the link between rhetoric and reality, and what inclusion ‘looked like’, ‘felt like’, and ‘sounded like’ at each site, and whether any discernible differences could be attributed to societal culture. A refractive phenomenological case study approach was used to capture the messages within each context and the lived experiences of the participants as they sought to cater for the needs of students. Data were collected from semi-structured interviews with school leaders and teaching staff. Each researcher conducted environmental observations, documenting the impressions and insights gained from the more implicit messages communicated verbally, non-verbally, and experientially from school structures, visuals, and school ground interactions. Themes were collated from the various narratives that were recounted. Both similarities and distinct socio-cultural differences emerged.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.045 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| 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".