Sustainable school capacity building – one step back, two steps forward?
Bibliographic record
Abstract
Purpose This paper aims to serve as an introduction to and overview of this special issue of the Journal of Educational Administration entitled “Building organisational capacity in school education”. The co‐editors have solicited contributions from authors in Wales, Australia, Canada, the USA, England, Hong Kong and New Zealand. Design/methodology/approach The paper reviews past and contemporary approaches to the issue of capacity building in education and in particular, sustainable capacity building. As well as reviewing key researchers and writers in this field, including their own work, the authors foreshadow and synthesise the other seven papers that make up this special issue. Findings The paper contends that building capacity in schools and schooling, while no means easy, can be both understood and accomplished. However, caution needs to be exercised because hard‐fought gains in capacity building and sustainability can be quickly eroded under the influence of poor leadership or extraneous changes. Practical implications The paper serves as a framework both for the seven papers that follow and more generally for understanding and conceptualising sustainable school capacity building. Originality/value The paper performs the function of framing current debates and pressures around sustainable school capacity‐building in an international theoretical and practical context.
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 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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".