TANGGUNG JAWAB HUKUM BAGI PELAKU USAHA ATAS BEREDARNYA PRODUK JAMUR ENOKI YANG TERKONTAMINASI BAKTERI LISTERIA MONOCYTOGENES
Bibliographic record
Abstract
On April 2020, the Ministry of Food of Republic of Indonesia received news from the International Food Safety Authorities Network (INFOSAN) that from March to April 2020 in the United States, Australia and Canada, an extraordinary condition occurred after their citizens consumed Enoki Mushrooms that contaminated by Listeria Monocytogenes Bacteria from South Korea. The similar product are also circulated in several Indonesian’s supermarlet and e-commerce, it can it can cause a Listeria outbreak if there is not legal action as soon as possible. The research method uses in this thesis is a normative or doctrinal type, which is a process to find a rule of law, legal principles, and legal doctrines to answer the legal issues being discussed. The results of the research in this thesis are, first, the form of legal responsibility for business actors for the circulation of enoki mushroom products that contaminated by Listeria Monocytogenes Bacteria as is stated in Clause 19 of Law Number 8 of 1999 concerning Consumer Protection. Second, the standardization of the circulation of imported food products in Indonesia in accordance with Clause 57 of Law Number 7 of 2014 concerning Trading which regulates the provisions of Indonesian National Standards hereinafter referred as as SNI. Keywords: Legal Responsibilities, Circulation, Contaminated Listeria Monocytogenes
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".