WHAT INFLUENCES JOB SATISFACTION IN THE NEW NORMAL? INSIGHTS FROM EDUCATORS OF INDUSTRIAL TECHNOLOGY UNIT/COLLEGE IN A STATE UNIVERSITY
Bibliographic record
Abstract
Abstract This study on job satisfaction among educators in an Industrial Technology Unit/College of a State University in the new normal education setting was a descriptive research; conducted during the first quarter year 2024 among instructors and professors. This study showed that majority of the respondents are male, Master’s degree holder, and teaching in the University for less than two decade as Instructor 1. This study utilized the Job Satisfaction Survey (JSS) of Paul E. Spector (1985) with nine facet scale to assess employee attitudes about the job satisfaction. The instructor/professor-respondents strongly agreed that the Nature of Work was strongly approved aspect/facet of job satisfaction. The respondents find enjoyment in what they are doing (teaching and other services), hence their actions and practices manifest commitment, dedication and satisfaction. The job satisfaction of the instructors/professors was also contributed by facets Supervision, Coworkers and Communication. The findings clearly shows that the social aspects of the facets of the JS survey (Spector, 1985) were more emphasized and valued by the respondents and influenced their satisfaction in their respective tasks, teaching loads and other endeavors in the university such as research, extension, production and other projects and activities.
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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 | 0.002 | 0.001 |
| 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".