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Record W2166435775 · doi:10.1177/026272800702700302

The Darbār, the British, and the Runaway Mahārāja

2007· article· en· W2166435775 on OpenAlexaff
Shandip Saha

Bibliographic record

VenueSouth Asia Research · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPilgrimageHinduismPoliticsNorth indiaReligious studiesSociologyHistoryEconomic historyLawAncient historyPolitical scienceEthnologyPhilosophy

Abstract

fetched live from OpenAlex

The Vallabha Sampradāya or Pusti Mārga Hindu devotional community was founded in the sixteenth century by the Vaisnavite philosopher, Vallabha. His successors, known as mahārājas, continued to spread the teachings of the Pusti Mārga and enjoyed much success in Rajasthan and Gujarat. Political and economic patronage by elites of Western India soon transformed these mahārājas into wealthy landlords whose affluent lifestyles would cause much controversy in the nineteenth century. This article uses unpublished documents found in the National Archives of India to detail one of these controversies, revolving around struggles between the Pusti Mārga, the royal house of Mevād., and British authorities for control of the wealth associated with Nāthdvārā, the central focus of Pusti Mārga pilgrimage in Rajasthan. The protracted struggle for the control of Nāthdvārā indicates how Hindu spiritual leaders were far from passive observers of the world who gave themselves purely to the cultivation of spiritual pursuits. On the contrary, this particular controversy is a striking example of how the active engagement of religious leaders in local and regional politics could profoundly affect the existing religious and social structures of their time.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.087
GPT teacher head0.427
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2007
Admission routes1
Has abstractyes

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