Bibliographic record
Abstract
Collaboration between central and regional leadership is important to achieve common goals and improve the quality of life of the community. The purpose of this study is to find out how leadership is implemented in the regions. The method used is the study of literature from various sources, such as books and scientific journal articles. The results of the discussion are as follows: Some of the advantages of collaboration between central and regional leadership Better use of resources: With collaboration between the center and the regions, existing resources can be better utilized to achieve common goals. The two can support each other in terms of financing, human resources, and technology. Government performance improvement: Collaboration between the center and the regions can help improve government performance in terms of public services and better decision making. Both can learn from each other and exchange experiences to improve overall government performance. Increasing community participation: With collaboration between the center and the regions, community participation can increase. Communities are becoming more involved in decision-making processes and implementing programs implemented by the government. Improved coordination and synergy: Collaboration between the center and regions can improve coordination and synergy between various government agencies. This can help accelerate the implementation of programs and policies taken. Improving development equity: Collaboration between the center and the regions can help increase the distribution of development in all regions of Indonesia. With good coordination, programs and policies can be implemented more evenly and reach all regions in Indonesia.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".