Bibliographic record
Abstract
"This book analyses cultural questions related to representations of the body in South Asian traditions, human perceptions and attitudes toward the body in religious and cultural contexts, as well as the processes of interpreting notions of the body in religious and literary texts. Utilising an interdisciplinary perspective by means of textual study and ideological analysis, anthropological analysis, and phenomenological analysis, the book explores both insider- and outsider perspectives and issues related to the body from the 2nd century CE up to the present-day. Chapters assess various aspects of the body including processes of embodiment and questions of mythologizing the divine body and othering the human body, as revealed in the literatures and cultures of South Asia. The book analyses notions of mythologizing and "othering" of the body as a powerful ideological discourse, which empowers or marginalizes at all levels of the human condition. Offering a deep insight into the study of religion and issues of the body in South Asian literature, religion and culture, this book will be of interest to academics in the fields of South Asian studies, South Asian religions, South Asian literatures, cultural studies, philosophy and comparative literature"--
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".