Conflict Rate and Job Satisfaction among Staffs in the Islamic Republic of Iran: Rafsanjan Township as a Case Study
Bibliographic record
Abstract
This study is to investigate conflict rate and job satisfaction among staffs in the public organizations in Rafsanjan Township in the Islamic republic of Iran. In this study, staffs personality, laws and guide directions, staffs needs, communications and job satisfaction is investigated by examining the staffs’ salary, job nature, supervisor and salary of staffs’ colleagues and their promotion. This was a quantitative study. All data of this study have been collected by using questioner. All data of this study have been analyzed quantitatively by using the SPSS software. Statistical society of the study was people who have worked in the public organizations in Rafsanjan Township. This study has chosen 385 staffs and has distributed two types of questionnaires namely job satisfaction and conflict questioners among them randomly. Finally, this study has concluded that1. There is a reciprocal relationship between conflict rate and job satisfaction.2. There is a negative relationship between conflict rate and payments. 3. There is a reciprocal relationship between conflict rate and job nature.4. There is a negative relationship between conflict rate and supervision style.5. There is a negative relationship between conflict rate and colleagues.6. There is a reciprocal relationship between conflict rate and promotion opportunity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".