Kajian Kinerja Pelaksanaan Pengurusan Persetujuan Bangunan Gedung (PBG) pada Dinas PUPR Kabupaten Padang Pariaman
Bibliographic record
Abstract
In 2022, the implementation of PBG processing which has been carried out by the Padang Pariaman DPUPR in the field of Human Settlement does not comply with the time period based on the established regulations. In this regard, the author conducted research on the factors that influence its implementation, the obstacles faced, as well as potential improvements or recommendations to increase the efficiency and effectiveness of the process. The research method used is a qualitative method by distributing questionnaires and interviews with informants. The dominant factor is timeliness in implementing PBG arrangements; facilities and infrastructure; HR; and initiative. In the Likert Scale assessment, the highest result was obtained in the Disagree assessment, namely 5.57%. The results of the identification of indicators and evaluation results have been validated with experts so that the author provides recommendations to the field of creative work of the Padang Pariaman Regency DPUPR to increase the number of human resources capable of carrying out PBG implementation in accordance with applicable regulations. The Padang Pariaman Regency DPUPR Job Creation Sector must have the facilities and infrastructure to carry out the duties of implementing PBG in serving the community. And the Cipta Karya sector must have the initiative to provide information to the public about PBG and provide licensed experts to help the community complete the PBG requirements.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".