Communication and Coordination Innovations in Improving the Performance of Permits for Suitability for Spatial Use Activities
Bibliographic record
Abstract
In the permitting process for City Plan Information (KRK) and Suitability of Space Utilization Activities (KKPR), intensive communication is required between the two implementing agencies, namely, the Investment Service and One-Stop Integrated Services (DPMPTSP) and the Public Works Office for Spatial Planning.Housing and Settlement Areas (DPUPRPKP) and the Technical Service in processing permit applications recommending KRK and KKPR.Problems with long coordination and communication meetings in recommending requests for KRK and KKPR.The research aims to identify and analyze the coordination and communication in the KRK/KKPR licensing recommendation process.This study used a qualitative method by interviewing staff of the Implementing Service and applying for KRK/KKPR permits and secondary data through journals, textbooks, and Implementing Office data.The analytical approach utilized involves reducing data, presenting data, and making inferences or doing verification.Based on Standard Operating Procedures (SOP) and Government Regulation 21 of 2021 concerning Spatial Planning, this research produced recommendations for quick and consistent communication performance in licensing services issuing KRK and KKPR.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".