The Relationship between Organizational Climate and the Organizational Silence of Administrative Staff in Education Department
Bibliographic record
Abstract
The aim of the present study was to determine the relationship between organizational climate and the organizational silence of administrative staff in Education Department in Isfahan. The research method was descriptive and correlational-type method. The study population was administrative staff of Education Department in Isfahan during the school year 2014-2015 with a number of 517 staff, of whom a number of 220 staff were selected as the sample using stratified random sampling fit for the size and by means of Krejcie and Morgan’s (1970) sampling formula. Measurement instruments were Sussman and Deep’s (1989) Organizational Climate Questionnaire and Van Dyne et al. (2003) Organizational Silence Questionnaire. For data analysis, Pearson correlation coefficient, stepwise regression and multiple variance tests were utilized. The results indicated that there was an inverse and significant relationship between organizational climate, bonuses in organization (r=-0.163 and P=<0.05) and procedures in organization (r= -0.196 and P=<0.01), and organizational silence. The results of multiple regression indicated that the best predictors of organizational silence were procedures in organization and objectives of organization, respectively (P=<0.01) among other dimensions of organizational climate. The results of multivariate analysis of variance test showed that there was a significant difference in respondents’ opinions about organizational climate, considering their age.
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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 | 0.006 |
| 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) | 0.002 | 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".