The Comparison of the Empowerment of Administrative Personnel in University of Al-Khairiyah Cilegon and Stie Bina Bangsa Serang Banten Indonesia
Bibliographic record
Abstract
This study aims to find the comparison of the level of empowerment of administrative personnel in University of Al-Khairiyah Cilegon and Institute of Economics (STIE) Bina Bangsa Serang, Banten, Indonesia. This study conducts descriptive method, as it describes and interprets the data as they are. The data are the comparison of the level of empowerment of administrative personnel in University of Al-Khairiyah (Institute of Economics, Institute of Tarbiyah Science, and Institute of Computer Science) Cilegon and Institute of Economics (STIE) Bina Bangsa Serang, Banten, Indonesia. The samples of this study are 74 respondents that consist of 37 respondents of Al-Khairiyah University Cilegon and 37 respondents of STIE Bina Bangsa Serang. The result shows that the d.b. = 72, and at the trust level of 95%, the t tabel obtained is 1.67. Therefore, it can be seen that the t value (0.043) is smaller than t table (1.67). According to the result, it can be concluded that the hypothesis which says “there is a significant difference between the level of empowerment of administrative personnel in the University of Al-Khairiyah Cilegon and Institute of Economics (STIE) Bina Bangsa Serang” is unacceptable. But, score mean variable Y (Institute of Economics Bina Bangsa) less more than score mean variable X (University of Al-Khairiyah). This is supported by location university in the central of Serang City and human resources that qualified.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".