ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI EFEKTIVITAS PENYUSUNAN RENCANA PENARIKAN DANA (STUDI KASUS PADA UNIVERSITAS NEGERI SEMARANG)
Bibliographic record
Abstract
The purpose of this study is to analyze the effect of quality human resource, quality application, reward and punishment, organization management, and socialization and training on the effectiveness of the preparation of cash withdrawal planning. This research method uses primary data with a sample of 92 respondents. The analytical tool used is SmartPLS version 3.3.3. The results of the study show that the quality of human resources and reward and punishment have a positive effect on the effectiveness of the preparation of cash withdrawal planning. Application quality and organizational management have no effect on the effectiveness preparation of cash withdrawal planning. However, socialization and training have a negative effect on the effectiveness of the preparation of cash withdrawal planning. Keywords: cash withdrawal planning (RPD), quality human resource, quality application, reward and punishment, organization management, socialization and training
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".