CRIMINAL SANCTION CHARACTERISTICS AGAINST CORPORATION COMMITTED ON CONSUMER PROTECTION OFFENCES IN FEW COUNTRIES
Bibliographic record
Abstract
This study aims to analyze the character of the sanctions system against corporations that commit consumer protection crimes. This paper is novel because it seeks to contribute to the current debate in the literature about sanctioned individuals and corporations by comparing the sanction and fines in few countries. Criminal sanctions in force in a country are very dependent on the state's reaction to the deviant activities of the corporations in the society concerned. The state's response was manifested in the sanction system policy towards corporations that commit criminal acts. A sanction system is still conventional, while a sanction system is responsive to the corporate phenomenon. Does this research discuss the sanction system's character against corporations that commit criminal acts of consumer protection? What are the problems of the system of sanctions against corporations that commit these consumer protection crimes? This research was conducted with a normative and comparative approach to comparing the sanctions against corporations that commit criminal acts of consumer protection between the Indonesian Consumer Protection Act and the Consumer Protection Act in several countries, namely Malaysia, the Philippines, Canada, and Finland. This study indicated differences in the character of the criminal sanction system between the Consumer Protection Act of Malaysia, the Philippines, Canada, Finland, and Indonesia against corporations that commit criminal acts in consumer protection. Another characteristic found is a single formulation, namely the threat of criminal fines except for the Indonesian Consumer Protection Act.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".