Educational leadership policies and practices: Voices from developing countries
Bibliographic record
Abstract
Educational Leadership Policies and Practices highlights voices from different developing countries that echo the need for sustainable, enabling, and liberating educational leadership that will stimulate ideas and ideals to usher new ways of looking at old problems of educational leadership. The chapters, largely, are based on original empirical field research, learnings drawn from applied research, and study of organizational learning. In addition, they are based on policy analysis and analytical deconstruction of the mind-boggling nuances of pedagogical, transformational, or transforming leadership theories. In an area where so little has been written on school and system leaders, Educational Leadership Policies and Practices: Voices from the Developing Countries is a very welcome contribution to the field. The various authors do a great job of portraying how radically different the contexts are for making education progress as leaders. We see the familiar concepts: transformational, moral, pedagogical, capacity building, contingent, mobilizing community, and so on, but the contexts are so different that the findings and lessons generate new ideas about leadership. The six main leadership lessons for less developed countries examined in the final chapter are especially powerful. Michael Fullan, Professor Emeritus, OISE/University of Toronto, Canada
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".