Pencemaran Nama Baik Melalui Sarana Informasi dan Transaksi Elektronik (Kajian Putusan MK No. 2/PUU-VII/2009)
Bibliographic record
Abstract
Libel crime is an offence attacking the honor and image of person. There are at least two elements in the libel crime in which a judge has an obligation to prove them, subjective and objective element as well as malice. An offender cannot be blamed for his/her conduct unless he/she commits these elements. In the term of article 27 (3) of electronic transaction and information act no 11 of 2008, its content is still in accordance with the rule of law conception and several articles of Indonesia Constitution of 1945 dealing with some fundamental rights of citizen and the right of freedom to express and to obtain information. State has untitled to make any limitation by prohibiting certain activities attacking the honor and image of person which is based on the same rights of the same freedom.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.026 |
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".