Collaborative Inquiry and School Leadership Growth: An Australian Adaptation of an Albertan Approach
Bibliographic record
Abstract
This article introduces an Australian adaptation of an approach to supporting school leadership and improvement pioneered by educationalists David Townsend and Pam Adams, from Alberta, Canada. Referred to as the North Coast Initiative for School Improvement, the adaptation involved school leaders, academics, and government officials who combined to implement the twin processes of collaborative inquiry and generative dialogue at about sixty primary and secondary schools across the North Coast region of New South Wales, Australia. The initiative appears from all reports to have been both well received and highly impactful, including in terms of improved student performance. This issue of the AJER offers an exploration from an Australian perspective of the principles underpinning the two processes. It also presents case studies from an Australian setting of the impact of these processes on school leadership and improvement. Key words: collaborative inquiry, generative dialogue, North Coast Initiative for School Improvement, school leadership, school improvement Cet article présente l'adaptation australienne d'une approche de soutien à la direction et à l'amélioration des écoles mise au point par les pédagogues David Townsend et Pam Adams de l'Alberta, au Canada. Connue sous le nom de North Coast Initiative for School Improvement, cette adaptation a impliqué des leaders scolaires, des universitaires et des représentants du gouvernement qui se sont associés pour mettre en œuvre les processus jumeaux d'enquête collaborative et de dialogue génératif dans une soixantaine d'écoles primaires et secondaires de la région de la côte nord de la Nouvelle-Galles du Sud, en Australie. D'après tous les rapports, l'initiative semble avoir été à la fois bien accueillie et très efficace, notamment en termes d'amélioration des résultats des élèves. Ce numéro de l'AJER propose une exploration d'un point de vue australien des principes qui sous-tendent les deux processus. Il présente également des études de cas d'un contexte australien de l'impact de ces processus sur la direction et l'amélioration des écoles. Mots clés : enquête collaborative, dialogue génératif, leadership scolaire, amélioration des écoles, North Coast Initiative for School Improvement
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| 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".