Penegakan Perda Kota Denpasar di Kawasan Prostitusi Belanjong Sanur
Bibliographic record
Abstract
Social problems related to prostitution continue to develop from various cities, as well as the city of Denpasar as a metropolitan city where there is a place of prostitution in one of the Denpasar areas, namely Belanjong Sanur. With the Regional Regulation (Perda) of Denpasar City Number 1 of 2015 concerning Public Order, it is hoped that its implementation. Based on the background of the problem above, the purpose of this study is to determine the implementation of the Regional Regulation (Perda) of Denpasar City Number 1 of 2015 concerning Public Order in the Belanjong Sanur area and to analyze how the efforts made by the Denpasar City Government in eradicating prostitution in the Belanjong area Sanur. This type of research is empirical legal research using descriptive qualitative data analysis methods. The results showed that the implementation of the Denpasar City Regional Regulation (Perda) Number 1 of 2015 concerning Public Order in the Belanjong Sanur area, which was implemented by the Denpasar City Civil Service Police Unit (Satpol PP) has been carried out well. Furthermore, the efforts made by the Denpasar City Government in eradicating prostitution in the Belanjong Sanur area, in general, the efforts to overcome prostitution can be divided into two, namely efforts that are preventive in nature and actions that are repressive in nature. Preventively in the Belanjong Sanur area, namely conducting socialization and counseling. The law enforcement carried out by Satpol PP is by controlling the location of prostitution and making arrests, as is their obligation as the enforcer of Perda No.1 of 2015.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".