Analisis Penguatan Dinas Perhubungan Dalam Pengawasan Perizinan Angkutan Kota Di Kota Medan
Bibliographic record
Abstract
The Medan City Transportation Service is the implementing element of the Medan City Government in the field of transportation, having duties in the field of public transportation services. City transportation is one of the modes of transportation that is still the focus of improving the government and public transportation service providers. The problems studied are how to regulate the mechanism for controlling urban transportation licensing in the city of Medan and how the supervision of the Department of Transportation on the licensing of city transportation in the city of Medan. The research conducted is empirical research, namely by conducting interviews. The data collection method in this research is library research and field research. The data analysis used is qualitative data. The legal regulation of urban transportation in Medan City is Law no. 22 of 2009 concerning LLAJ and Medan City Regulation No. 2 of 2014 concerning Regional Levies in the Field of Transportation and Perwal of Medan City No. 41 of 2018 concerning Delegation of Partial Licensing and Non-Licensing Authorities to the Head of the Medan City Investment and One-Stop Integrated Service Office. Supervision of the Department of Transportation on city transportation permits in the city of Medan is carried out in two ways, namely: internal supervision and external supervision. In carrying out the task of supervising and controlling transportation in the city of Medan, the Medan City Transportation Service coordinates with the Traffic Traffic Unit 2 (two) times a year, namely in June and November.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".