Factors Affecting Teachers Job Satisfaction in Case of Wachemo University
Bibliographic record
Abstract
This study was design to assess factors that affect teachers’ job satisfaction in Wachemo University. To meet this objective, the researcher drew 768 in teachers in male are 663 and the rest of female are 105 in number. In order to make the study the researcher was select 54 males and 34 female’s teachers to determine sample size by using simple random sampling method. The main objective of this study was to assess and explore the factors that contribute to job satisfaction. The basic research question of this paper was first, what factors affect the teachers’ job satisfaction in working place second, what is negative the job satisfaction of teachers in working place Third, what mechanism are helps to reduce the existence of specific factors. So, the data collected is through questionnaire and interview. Finally, the collected data were analyzed by using table and percentage. The result revealed that the major work related factors that affect teachers’ job satisfaction were salary stressful job, overtime work without payment, relationship with top management opportunities for advancement, chance for promotion, and availability of teaching learning materials and rules and regulation of the campus. The study suggested that it is advisable to the concerned bodies especially the management organ of Wachemo University should give on attention to those factors and should plan different strategies to improve teachers’ job satisfaction and there must be motivational and incentive strategies to strengthen and motivated teachers and to bring job satisfaction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".