Pengaruh Anggaran Berbasis Kinerja dan Sarana Prasarana terhadap Motivasi Serta Dampaknya pada Kinerja Anggota Polri di Kepolisian Resor Tanjung Jabung Barat
Bibliographic record
Abstract
This research was conducted on members of the National Police in the Tanjung Jabung Barat Resort Police, which can be seen from the ability of human resources or officers who are able to complete work related to all activities in the organization and public services, including all activities that exist in the ranks of members of the National Police in the resort police (Polres) Tanjung Jabung Barat. This study uses descriptive and verificative analysis method, descriptive analysis used to describe respondent characteristics and research variables without conducting testing. The verificative analysis is used to determine the magnitude of the influence between independent variables on dependent variables. This study uses census method that is all members of the National Police in the resort police (Polres) Tanjung Jabung Barat, in addition to the data used in this study is the primary data obtained from the results of questionnaires provided and have been filled by each member of the Police in the resort police (Polres) Tanjung Jabung Barat. The data analysis technique used is path analysis, with the help of SPSS software program version 22.0. The results of this study show that the free variables of performance-based budgets and infrastructures positively and significantly affect motivation and have a positive impact on the performance of members of the National Police in the resort police (Polres) Tanjung Jabung Barat, this proves that with the increasing budget-based performance and infrastructure of members of the National Police in the resort police (Polres) Tanjung Jabung Barat, it will also increase the motivation of members that will have an impact on improving the performance of police members in the resort police (Polres) Tanjung Jabung Barat.
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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 | 0.002 | 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) | 0.019 | 0.003 |
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".