The Relationship between Teachers’ School Commitment and School Culture
Bibliographic record
Abstract
The study, which aims to put forth the relationship between school commitment and school culture, is designed on causal-comparative, relational survey model. The study group is composed of 150 voluntary teachers working in Aydin. In the research, “organizational culture” and “organizational commitment” scales are used. In the analysis of the data, descriptive and proof testing statistical techniques are used. At the end of the study, it is found that teachers have affective commitment perceptions at the highest level, and bureaucratic culture perceptions at the lowest. While the perceptions of the teachers related to school commitment and culture does not show any significant difference according to their gender, educational status and specialty; these perceptions show a significant difference according to seniority, type of school, and length of service at the same school. It is detected that affective commitment is predicted by task culture; continuance commitment is predicted by achievement and support culture; and normative commitment is predicted by support culture significantly. All the dimensions of organizational culture predict affective commitment at the highest level. Together with the fact that school culture is an effective factor in teachers’ commitment to their school, some suggestions are given towards developing school culture based on especially support and achievement culture.
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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".