Study of the Relationship Between School Managers’ Communicative Skills and Schools’ Atmosphere
Bibliographic record
Abstract
Schools are places where social interactions take place highest. Teachers, students and parents are the individuals of school management that constitute this social life place. A school’s success is closely related with its workers’ fulfilling the duties that are expected from them within an interaction. Every school has its own unique atmosphere. Many factors play an important role for this atmosphere to emerge, however the most important of these is communication skills. Hence for a school’s expected success to be accomplished a strong communication must be created. In that sense a school manager’s communicative skill is an important factor for a school’s success to be accomplished. In this research, school managers’ communicative skills on schools’ atmospheres are studied among the communication processes that exist in schools. Decreasing the problems to minimum that stem from communication, what school mangers can and may do to improve their communicative skills are explained, discussed and ordered in this research. The aim of this research is to determine the effects of school managers’ communication skills on a school’s atmosphere. Relational screening model among quantitative methods were used in this research. Target population of the study consists of teachers and school manager assistans commisioned in official state schools in Istanbul from the districts of Sultanbeyli, Tuzla and Pendik. In this target population, total of 378 teachers constituted the sampling group, determined through random selection method.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".