Pengaruh Kualitas Sumber Daya Manusia, Sarana Pendukung Dan Komitmen Pimpinan Terhadap Kinerja Satuan Kerja Perangkat Daerah (SKPD) Dalam Penyusunan Laporan Keuangan Skpd Di Lingkungan Pemerintah Provinsi Sulawesi Utara
Bibliographic record
Abstract
This study on the effect of human resources, means of support and commitment to the performance of work units (SKPD) in the preparation of financial statements in the North Sulawesi provincial government, such research is still relatively small, and the results of the study are still varied and inconsistent. The purpose of this research was conducted to find whether there is empirical evidence Effects of human resources, means of support and commitment to performance on education in the preparation of financial statements in Sulawesi Utara.Populasi this study are all available on education in the government of North Sulawesi province. The unit of analysis is the head of the organizational work units. Data was collected through questionnaires delivered directly by the author. Before testing the hypothesis with multiple regression analysis, prior testing and test data quality classical assumptions. The results showed that the partial human resources, means of support and commitment to influence performance on education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".