Collaborative Inquiry Driving Leadership Growth and School Improvement
Bibliographic record
Abstract
Australia’s largest schooling system, the NSW Department of Education, is in a period of unprecedented change as the Department of Education initiates a range of reforms. One critical reform occurred in 2014 when the Department of Education and the New South Wales Teachers’ Federation agreed to link teachers’ salaries with accreditation. For the first time, all Department of Education principals, executives and teachers must complete an annual Performance and Development Plan. This article describes the work of a team of academics from the School of Education, Southern Cross University, and the Department of Education school leaders in northern NSW, exploring opportunities to accomplish school improvement through the “North Coast Initiative for School Improvement” (NCISI). The impetus for this initiative is based on the work of Alberta academics and researchers, Dr. David Townsend and Dr. Pamela Adams. The approach is based upon small teams, comprising a member of a school district’s central office, a district principal and university academics, who once a month visit the leadership team of a school in order to build instructional leadership. This process involves the use of a guiding question, generative dialogue and a collaborative inquiry methodology. Early findings indicate the NCISI’s approach is having positive impact leadership growth, through collaboration. Key elements of trust and professional identity have developed within teams. The very positive reaction of school communities to the project in its early stages is heartening and shows that there is a strong desire by school leaders to draw upon collaborative support in order to grow professionally. The project also demonstrates a strong level of commitment from a regional university to build productive relationships with schools.
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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 | 0.056 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.046 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".