Part of the Educational Administration and Supervision Commons, and the Elementary Education and Teaching Commons Recommended Citation
Bibliographic record
Abstract
This paper examines the role of effective leaders in bringing about quality in teaching and learning in schools. It is based on my doctoral empirical research undertaken during 2000-2005 under the auspices of the University of Toronto’s Ontario Institute of Studies in Education (OISE/UT), Canada. My study explores the roles, beliefs and behaviors of three reputationally effective secondary school headteachers in Karachi, Pakistan, in three types of schools – government, community and independent, assuming that contextual factors will influence the nature of leadership. The findings reveal that all three heads ’ beliefs and practices show similarity in a vision of providing quality education, balanced between Islamic teachings and values, and modern, secular content and skills. As managers and leaders, the heads focused on building an environment conducive to better teaching and learning, enabling teacher development, and fostering productive relations within and outside their schools. They differed, however, in their rationale, strategies and application of these strategies, due largely to differences in their personal histories, specific beliefs and values, and organizational settings.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.421 | 0.147 |
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; the direct Gemma label and the distilled Codex classifier 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".