Violence in the Valmiki Ramayana: Just War Criteria in an Ancient Indian Epic
Bibliographic record
Abstract
When is armed force considered justified in Hinduism? How do Hindu legitimizations of warfare compare with those of other religions? The Just War framework, which evolved from Roman and early Christian thought, stipulates distinct criteria for sanctioning the use of force. Are those themes comparable to the discourse on violence of ancient India? This article examines the influential Sanskrit epic Vālmı̄ki Rāmāyaṇa in order to broach these questions. This analysis demonstrates the presence in the ancient work of all seven modern Just War criteria—namely (1) Just Cause, (2) Right Intent, (3) Net Benefit, (4) Legitimate Authority, (5) Last Resort, (6) Proportionality of Means, and (7) Right Conduct. This study also shows the extent to which the criteria and the larger discourse in the Vālmı̄ki Rāmāyaṇa are distinctly couched within Indic ethical parameters, drawing particularly upon the moral precept of ahiṃsā (nonviolence). This article identifies both similarities and differences between the epic's criteria for warfare and those of the Just War framework. By comparing representations of violence in the Vālmı̄ki Rāmāyaṇa to modern Western legitimizations of force, this study advances the inclusion of Hindu thought into the global discourse on the ethics of war and peace.
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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 | 0.002 | 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 | 0.008 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".