Bibliographic record
Abstract
The purpose of this research is to reveal the contents of civil law regarding business competition and social lessons from the prohibition of unfair business competition (monopoly and other fraud) contained in Indonesian government policies. The research method used is qualitative content analysis with a normative juridical approach using the keyword 'Policy related to business competition.' The results of this study indicate two findings. First, Law No. 5/1999 concerning the Prohibition of Monopolistic Practices and Unfair Business Competition is an implementation of the politics of business competition law in Indonesia. In principle, the politics of business competition law in Indonesia depends on the political will of the House of Representatives (DPR) as the legislative body together with the Government as the executive in making laws. Because Law Number 5 of 1999 concerning the Prohibition of Monopolistic Practices and Unfair Business Competition is not yet effective enough in creating fair business competition in Indonesia because, in substance, the Law still contains weaknesses in several articles that make the performance of the Business Competition Supervisory. The commission is not maximal. Second, government policies contained in the Civil Code, KUHP, Law no. 5 of 1984 concerning Provisions for Main Industries, Law no. 8/1995 concerning Capital Market, Law no. 9 of 1995 concerning Small Businesses, and Law No. 36 of Telecommunications provide important lessons regarding the prohibition of monopolies and fraudulent practices that can hinder the economy and equitable social welfare. The expected implication is that social learning from government policies in the field of law regarding deregulation, investment, and other policies aimed at supporting business competition can promote sustainable development, particularly in industry, small businesses, capital markets, and telecommunications.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".