Perception Based Principal Leadership Measurement: Does It Work in Pakistan?
Bibliographic record
Abstract
Adequacy of relevant knowledge and interpersonal skills, interalia, are vital contributors to a successful leadership. However, leadership is not a self-centered phenomenon but followers through their actions and views profess it. Extant literature suggests teachers’ and principals’ perceptions as a prominent tool for measuring school leadership. This study evaluated the applicability of this leadership measure in context of Pakistani schools. Study focused on analyzing the differences between views of school principals about their own leadership attributes and as viewed by the teachers under their sub-ordination. We adopted the questionnaire developed by Sharma (2010) and Sun et al. (2014) to collect the data on leadership attributes. Total of 178 teachers and 18 principals from Secondary Schools in Pakistan responded the questionnaire. We applied independent sample t-test to analyze the data. The results showed a significant difference between perceptions of principals about their own leadership attributes and that of views by the teachers working under them. The results of the study suggest that principals in the schools under study lack the leadership skills. Further analysis revealed that most of the principals considered them good leader but their sub-ordinates opinion negated this. It shows, the principals, under study, either lacked the interpersonal skills to motivate their sub-ordinates or failed to modify the opinion of their sub-ordinates.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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; 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".