Our Right to Share, Their Right to Know: An Analysis of Public Interest Defense to Defamation
Bibliographic record
Abstract
The defamation reports have increased and shifted under online-based technology through social media. This study considered the defamation issue in Indonesia that alleged Richard Lee, a doctor who shared a beneficial publication through social media about the dangerous skincare product. Richard's audience believed that his content helped them know the hidden truth behind skincare products available in the market. Consequently, the public questioned whether he was liable because he was regarded to share helpful information under the public interest. This study aimed to analyze Indonesia’s defamation laws, especially in public interest defense under Article 310(3) of the Indonesian Criminal Code. However, the interpretation for public interest as a crime abolition is unclear, resulting in various courts' decisions that lead to criminalizing internet users. This study used legal research with statutory and comparative approaches. It examined legal norms and practices in Indonesia and compared those in the United Kingdom, Canada, and New Zealand. These three countries adapted defamation law to develop cases, including those alleged defamations for the public interest. While the freedom of expression is enshrined in the constitution, its practice has contradicted defamation provisions outlined in derivative regulations. By comparison, these three countries have precise boundaries and public interest defense is explicit. Those countries have specific rules and lists that needed to be fulfilled for those who use public interest defense. The lists based on previous precedents show how they learn and adapt to the development of public interest defense in many cases. This study concluded that Indonesia does not have specific standards or rules to determine cases categorized as the public interest. KEYWORDS: Public Interest Defense, Online Defamation, Freedom of Expression.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".