The Influence Of Work Ability, Work Motivation, Work Situation, Job Satisfaction And Compensation System On Work Achievement Of Employees In The District Of Transportation Of Muara Enim
Bibliographic record
Abstract
Research with the aim to determine and analyze the effect of work ability, work motivation, work situation, job satisfaction and compensation system partially and simultaneously on the work performance of employees of the Muara Enim District Transportation Office. The research method used in this study is a quantitative method using questionnaires distributed to respondents. Test the quality of the data by using the validity test, reliability test, normality test. The analysis used is descriptive analysis and inferential analysis using statistical calculations with multiple regression formulas, partial test simultaneous test and the coefficient of determination. The results of the study showed that work ability has a significant effect on the work performance of employees of the Muara Enim District Transportation Office. Work motivation has a significant effect on work performance. Work situation has a significant effect on work performance. Job satisfaction has a significant effect on work performance. The compensation system has a significant effect on work performance. Work ability, work motivation, work situation, job satisfaction and the compensation system have a jointly significant effect on the work performance of employees of the Muara Enim District Transportation Office.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".