DEFILING THE DEAD: - UNDERSTANDING NECROPHILIA UNDER THE INDIAN LEGAL FRAMEWORK.
Bibliographic record
Abstract
The term “Necrophilia” originates from the Greek language, whereby the word nekrosmeans “dead” or “dead body” and the word philios means “attraction to”. Necrophilia in general sense describes the uncanny mental condition in which the perpetrator obtains pleasure in establishing sexual relationships with the corpses.Thus, the obnoxious act of Necrophilia is not only detrimental to the right to dignity of the dead, but also causes a despondency within the societal norms at large.\n\nAt the outset, this present research paper endeavours to adapt an interdisciplinary perspective in order to bring out various theories inclusive of psychoanalysis, socio-legal insight and a psychiatric review of the aforementioned act. This paper aims to include Nithari Serial-Murder case study to understand Necrophilia and its interpretation under the Indian Criminal Law. Further, the paper also outlines the view of majority of States namely: Brazil, Canada, U.K. etc. which address the commission of sexual intercourse or sexual attraction towards human corpses. The author(s) endeavour to demonstrate the current position pertaining to Necrophilia in the Indian Legal Framework by interpreting various sections and Articles in the Indian Laws. This paper is also an attempt to propose suggestions and recommendations for strengthening the laws and their penalties’ related to Necrophilia which is a heinous crime jeopardizing the dignity of deceased.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".