The Application Degree of Participative School Leaderships at Al-Ihsa Governorate and Its Correlation with Teachers’ Professional Development
Bibliographic record
Abstract
Participative leadership is one of the most important human trends of school leadership and institutions of education. The study aimed to identify the degree of application of participative leadership by school leaders at Ihsa governorate, Saudi Arabia, and its correlation to teacher’s professional development in the light of some variables. The study sample comprised (241) education leaders from both sexes throughout the school year 2018/2019. To collect data, an important three-part instrument developed incorporating participative leadership and its correlation to teacher’s professional development. Cronbach coefficient of instrument validation was (0.97). In analyzing data, arithmetic means, standard deviations, one way ANOVA, and correlation coefficient were calculated. Results of the study showed that the degree of application of the total process was high. They also showed that there was a relation with statistical significance at the level (0.01) between participative leadership with its dimension and professional development. The results also showed that there were no differences with statistical significance in answers of sample members which might be attributed to study variables at the level (α=0.01). The study recommended intensifying training courses for school leaders with regard to participative leadership, in addition to, supporting and widening teachers’ participation in school leadership.
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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".