Bibliographic record
Abstract
This paper will report on the findings of four international research projects on leadership in high-performing school systems around the world. The paper will focus on building the capacity of school leaders to exercise professional autonomy and how different levels of government achieve strategic alignment among policies in their efforts to lift performance. The paper will summarise findings reported in The Autonomy Premium published in 2016 by ACER Press, along with the findings of a national survey of principals in Australia. The major part of this presentation is devoted to comparing Australia on 15 benchmarks derived from international studies in 2017 in Australia, Canada, China (Hong Kong), England, Estonia, Finland, Israel, Japan, Korea, New Zealand, Singapore and the United States. The key message will be that Australia will not become one of the top-10 high-performing systems unless there is a transformation of approaches to leadership and leadership development at all levels, and unless due account is taken of outstanding practice in schools and school systems around the nation. Innovation and the resourcefulness of leaders abounds but these must be scaled up. This paper will explore the challenges and priorities for governments and leaders in schools and school systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".