Bibliographic record
Abstract
This article examines and analyses the socio-political implications of the patronage and development of the Bengali guru-shrine of Belur Math, the headquarters of Ramakrishna Mission, by the central government of India. Founded by Swami Vivekananda – regarded as one of the most renowned Hindu cultural leaders of nineteenth century colonial Bengal – Belur Math is a symbol of cultural heritage for Bengali-Hindus. In 2017, Belur Math was the only temple from West Bengal to be selected for religious-tourism development under the Pilgrimage Rejuvenation and Spiritual Augmentation Drive (PRASAD) scheme, a national project launched by the Bharatiya Janata Party (BJP) and helmed by Narendra Modi, the prime minister of India. Under the PRASAD scheme, the math underwent a series of infrastructural developments, which also included accompanying political signage. I use these developments with their accompanying political signage as an entry-point for discussing the politicization of particular devotional sites by the BJP (the ruling party of India since 2014), and consider its various socio-political implications at both the regional and national levels. Using an ethnographic approach, the study asks: 1) What are the political implications of state patronage of a religious site with prominent display of political signage? 2) How do the new developments affect the experiences of everyday visitors at the site? 3) How do the lay devotees interpret the foray of political signage within a religious space? The study addresses these questions through field work observations and interviews with various actors, such as lay devotees and visitors to the shrine and state tourism officers. Through a close analysis of the PRASAD scheme’s implementation at the math and the devotees’ responses to it, I suggest that, although normalised as a government initiative to develop the religious site and promote religious-tourism, the project actually emerges as a well-coordinated political strategy: as the BJP attempts to make in-roads in Bengal’s regional political landscape, this project is meant to appeal to the middle-class Hindu-Bengali lay devotees of the guru-faith organization. Accordingly, this article provides insights into the BJP’s recent trend of capitalizing on Hindu gurus and sacred shrines as a specific and valued Hindu cultural good, and considers the various ways such alliances benefit both the political party and the religious organization.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.059 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".