The effect of employee performance through motivation and commitment on government tax officers
Bibliographic record
Abstract
The tax revenue in West Java has not fully realized and developed in accordance with the planned target. In line with reality, the employee performance in the Directorate General of Taxation should greatly determine the amount of tax revenue in West Java. Their performance can be measured based on the terms of quality, namely in achieving predetermined standards. While in the terms of quantity and responsibility, it can be measured based on their achievement on completion targets and following existing work procedures. This research method was carried out descriptively and verified. Assessment of scores on research variables is used as a descriptive method, while Path Analysis method is used both to aim and determine causality between research variables and hypothesis test. The descriptive result showed that Discipline, Compensation, Competency, Motivation, Commitment and Performance are categorized as ‘good’, but nonoptimal in their achievements. Verificative results showed that there are partially and simultaneously positive and significant effects between Discipline, Compensation, and Competency towards Motivation, both directly and indirectly. The most dominant effect on competency. Also, there are positive and significant effects between motivation and commitment towards employee performance in the West Java Regional I Office of Directorate General of Taxation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".