The Relationship between Physical Conditions of School Buildings and Organizational Commitment According to Teachers’ Perceptions
Bibliographic record
Abstract
The aim of this study is to determine the relationship between the physical conditions of school buildings andorganizational commitment according to the perceptions of teachers in public primary schools. The researchpopulation consists of 2450 teachers from 92 primary schools in the central district of Diyarbakır/Turkey in theacademic year of 2017-2018. The data collection instrument was applied to randomly selected 534 teachers from 27schools. “School Buildings Scale" developed by Çağlayan and Yılmaz (2011) and “Organizational CommitmentScale" developed by Meyer, Allen and Smith (1993), and adapted into Turkish by Dağlı, Elçiçek and Han (2017)were used in this study.Some important findings of the study are listed below: According to teachers' perceptions, the highest item that isassociated with the school buildings was found in the dimension of “General view (M=3,58; Quite adequate), theitem with the lowest level was found in the dimension of “Fields reserved for students” (M=2,44; insufficient). Themean of the whole scale was found as (M=2.99) “Partially adequate”. It was determined that the highest mean ofteachers' perceptions about organizational commitment (M=3.50; Agree) was in “affective commitment” dimensionand the lowest mean (M=2,94; Undecided) in the dimension of “normative commitment”. Teachers participated inthe total mean of the organizational commitment scale at the level of (M=3.19; Undecided). Generally, it was foundthat there was a moderate and positive relationship between the school building scale and organizational commitmentscale (r=,561, p <0.01). This shows that, as the physical conditions of the school buildings are improved, theorganizational commitment of the teachers is increased.
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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 | 0.003 |
| 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 | 0.001 |
| 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".