Defamation Law Basics: Understanding Slander and Libel in the Indian Perspective
Bibliographic record
Abstract
Defamation law in India addresses the protection of people's reputations against false and harmful statements, balancing this with the right to freedom of expression. This article explores the distinctions between slander (spoken defamation) and slander (written or published defamation), and the legal frameworks governing civil and criminal defamation in India. Examines the essential elements of defamation, such as falsehood, publication, harm and fault, and outlines key defences such as truth, good faith, public interest and privilege. Notable cases such as Subramanian Swamy v. Union of India and Rajagopal v. State of Tamil Nadu illustrate the judicial approach to defamation. The article also analyzes the impact of digital communication on defamation, addressing online defamation, jurisdictional challenges and the liability of intermediaries. Compares India's defamation law with that of other jurisdictions, such as the United States, the United Kingdom, Australia and Canada, highlighting emerging trends such as digital defamation, the role of AI in content moderation and the importance of international cooperation. Ultimately, the article highlights the need for balanced defamation laws that protect reputations while promoting freedom of expression in a rapidly evolving communications landscape.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".