Analisis Faktor-Faktor Yang Mempengaruhi Pengelolaan Aset di Institut Pemerintahan Dalam Negeri Kampus Papua
Bibliographic record
Abstract
The purpose of this study is to investigate the assets management in the Papua Campus of Institut Pemerintahan Dalam Negeri (IPDN). The research also wants to reveal how the influence of Legal Audit, Human Resources and Leadership Commitments on Optimizing Asset Management. We surveyed on this Campus by selecting a sample of 30 respondents. We empirically tested our hypothesis using Multiple Regression Analysis. The results show that the assets management on this Campus is proper and running under the appropriate statutes. Still, there are needs for more advance and thought in the assets administration, utilization and supervision. Legal audit proved to have a positive but not significant effect on asset management. It means that the audit does not guarantee asset optimization. Human resources and leadership commitments have a positive and significant impact on asset management, reflects that if human resources and leadership commitments are getting more robust, asset management will also be more trustworthy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".