Investigating the effect of empowerment aspects on the competence level and success of primary school principals
Bibliographic record
Abstract
The purpose of this study is to investigate the effect of empowerment aspects on the competence level and success of the primary school principals in Pakdasht city in Iran. This research is a descriptive-survey method and in terms purpose is practical. The statistical population of this study consisted of principals of the primary schools in Pakdasht city. The total number of primary school principals in this city is 135 people (75 male schools and 60 female schools). As data gathering tool, a researcher-made questionnaire was used. In order to analyze the validity of the questionnaire in this study, the Content Validation Method was used in which the questionnaires were first examined by the experts and the necessary corrections were made. The reliability of the questionnaire was evaluated using Cronbach's alpha coefficient. Research hypotheses were tested using structural equation modeling and AMOS software. The results show that competence and effectiveness aspects affect competence and success of principals of the Pakdasht elementary schools. Autonomy affects the competence level of elementary school principals in Pakdasht city but does not affect principals' success. Meaningfulness aspect did not affect principals' competency however it is effective on principals' success in Pakdasht elementary schools. Trust aspect was not effective on the Pakdasht elementary schools principals' competence and success.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".