PELATIHAN PEMBENTUKAN SISTEM PENGADUAN DAN KOTAK SARAN DARI MASYARAKAT UNTUK MENINGKATKAN KINERJA DESA
Bibliographic record
Abstract
Installation of suggestion boxes in public service offices in Indonesia is regulated in the Presidential Regulation of the Republic of Indonesia No. 76 of 2013 concerning public service complaints. The same thing is also stated in Law Number 25 of 2009 concerning public services, in this case referred to as a complaint box. The research method used is counseling, implementation, and evaluation. The data collection technique is by observation. The results showed that the suggestion box complaint service mechanism, namely the lack of socialization between the RT head and the surrounding community, therefore the authors wanted to form a suggestion box to convey aspirations from the community to improve the performance of the local RT, then it could be implemented again after the author left the community service location in the village. Bantarsari. The supporting factor is the mechanism with community participation in supporting the formation of the complaint system and the suggestion box, while the inhibiting factor is that there are still many people who do not realize the importance of the existence of the suggestion box.
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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".