Uplifting leadership for real school improvement—The North Coast Initiative for School Improvement: An Australian telling of a Canadian story
Bibliographic record
Abstract
This paper reports on a preliminary Australian adoption and adaptation, in the North Coast region of New South Wales, Australia, of the Townsend and Adams’ model of leadership growth for school improvement in Alberta. The Australian adaptation of this Alberta model has been named the North Coast Initiative for School Improvement (NCISI). The participants comprise nine university academics and almost one hundred regional school leaders. Leadership is developed through continuing and regular collaborative-inquiry and generative-dialogue meetings between the academics and school leaders. The aim is to improve school leadership with the primary purpose of improving student outcomes. Provisional evaluation records significant positive changes in school leadership across the region. Convergence and divergence of the Australian and Canadian models are explored. The Australian adaptation requires some modification to suit local education processes and context. In particular, there has been the development of some divergence in approaches, especially in working in individual schools or clusters of schools. While the program has only been running for a comparatively short time, and therefore formal program evaluation is only commencing, preliminary evidence suggests significant traction and success in the Australian context. The paper concludes with some tentative implications for the future development of this model in the Australian context: how can the model be conceptualised and delivered to a wider audience in the years ahead.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.041 | 0.025 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.013 |
| 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".