Justifying Violence: Ritual and Doctrine of Sacrificial Killing in Medieval Hindu Traditions
Bibliographic record
Abstract
To comprehend how sacrificial killing is justified within the Dharmaśāstric and Tantric Śaiva traditions amidst the pervasive influence of the doctrine of non-violence in South Asian religions, my project examines how sacrificial killing is constructed into rituals, doctrinally elaborated upon, and defended with dialogues across Brahmanical and non-Brahmanical traditions. This study is conducted through the textual analysis of five Sanskrit scriptures of various genres composed between the 9th and 17th centuries in Kashmir and Greater Bengal. Chapter One examines the justification of animal sacrifice in Medhātithi’s Manubhāṣya. The first section scrutinizes Medhātithi’s apologetics surrounding Brahmanical sacrifice, highlighting his scholarly elaboration on rationalizing sacrificial violence with the emphasis on the benefits bestowed upon the victim. The second section investigates Medhātithi’s criticism of animal sacrifice in customary practices, demonstrating that the non-Brahmanical sacrificial killing condemned by Medhātithi likely belongs to both Purāṇic and Tantric traditions. After examining the apologetics of sacrificial killing in the Dharmaśāstric tradition, my focus shifts to the rituals of killing in Tantric Śaivism. Chapter Two explores the three yogas of killing prescribed in the Netratantra. By contrasting the prescription of yogic killing in the Netratantra with accounts in the Brahmayāmala and Tantrasadbhāvatantra, I show that the Netratantra diminishes the emphasis on violence and bloodshed resulting from killing, while accentuating the salvific outcome for the victim. Chapters Three to Five examine sacrificial killing in the Tantrāloka and its commentary, analyzing the transformation of sacrificial killing within the doctrinal framework of non-dualistic Śaivism. I investigate how Abhinavagupta incorporates non-dualistic doctrine into the ritual and interpretation of sacrificial killing, thereby transforming it into an initiation of the victim through divine agency. Furthermore, I examine both the assimilation of and the resistance to Brahmanism revealed in the apologetics of sacrificial killing in Tantric Śaivism. Chapter Six briefly explores sacrificial killing post-Abhinavagupta by examining two scriptures composed in Greater Bengal: the Kālikāpurāṇa and the Māṃsatattvaviveka. Through this analysis, I argue that by incorporating rituals elements and apologetics from one another, the Brahmanical and Tantric traditions collaborate in responding to criticisms against sacrificial killing emanating from rival sects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".