The Who, How, Why, and What of Leadership in Secondary School Improvement: Lessons Learned in England
Bibliographic record
Abstract
Although arguments in scholarly journals claim that leadership is critical in initiating and sustaining school improvement, ambiguity surrounds the sources and role of leadership. In addition, little research documents how educators involved in school improvement perceive who leads, how, why, and for what purposes leadership is important. This article reports a case study of head teachers' and teachers' perspectives of leadership in an English secondary school involved in a university-based school improvement program. Specifically, we present a summary of the research as well as interpretations and themes constructed from the data analysis. Interpretations support recent theoretical claims that schools are complex organizations requiring multiple leaders and a distributed model of leadership accomplish improvement goals; and academic writing that urges a rethinking of school improvement. In concluding we argue that the development of professional expertise is to fostering successful schooling over time and call for a consideration of emergent perspectives of leadership in addressing issues related to influence and inclusion of teachers in goal-setting and leadership in school development.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".