Relationship between School Principals’ Ethical Leadership Behaviours and Positive Climate Practices
Bibliographic record
Abstract
The aim of this study was to determine the relationship between school principals’ ethical leadership behaviour and positive climate practices. Research sample consisted of 383 teachers working at schools affiliated to the Ministry of National Education in 2014-2015 academic year. This research was designed according to the relational model. Data was collected by using “Ethical leadership” and “Organizational climate” scales. The results of the research suggested that according to their perceptions, teachers respond to the ethical leadership levels of the school principals at the level of “I disagree” and to the positive climate practices at the level of “I partially agree”. It has also been established that female teachers found principals’ ethical leadership levels and positive climate practices higher than male teachers. The principals’ ethical leadership behaviours and positive climate practices did not show a significant difference in teachers’ seniority, age and years working with principal. A high level of significant positive relationship was found between the school principals’ ethical leadership levels and positive climate practices. In conclusion, it may be important for positive climate practice at school to raise the levels of ethical leadership behaviour of school principals. This may influence both the academic and social achievement of the students and behaviour of the teachers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".