Faktor Penghambat Penegakan Hukum Merek Di Sumatera Utara
Bibliographic record
Abstract
This research was conducted in order to identify the factors that become obstacles in the enforcement of criminal law marks in the jurisdiction of the Regional Police of North Sumatra (Poldasu). The method used in this research is normative-empirical law research (applied law research). A literature study was conducted on the legal literature, a field study was conducted on respondents with in-depth interviews with 2 officers from the Criminal Investigation Unit at Poldasu. The results of the research and discussion show that law enforcement is influenced by several factors, which then become obstacles in the implementation of the process, such as: 1) Legal factors, where the term that refers to criminal marks in Law no. 21 of 2016 concerning Marks and Geographical Indications (UU MIG) uses the word “violation” and there are provisions for complaint offenses; (Constitution); 2) Law enforcement factors, namely the parties that form and apply the law; 3) Factors of facilities and facilities that support law enforcement; 4) Community factors, namely the environment in which the law applies and is applied. 5) Cultural factors, namely as a result of work, creativity and taste based on human initiative in social life
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".