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The Radheshyam Ramayan in Text and Performance

2025· book· en· W7084067375 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicQualitative research in health
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)EthnographyHindiPeriod (music)Repetition (rhetorical device)Contrast (vision)

Abstract

fetched live from OpenAlex

Abstract A new Ramayan for a new age. In the first quarter of the twentieth century, a brahmin poet and singer-storyteller, Pandit Radheshyam Kathavachak (1890-1963), rewrote and published the classic story of the Ramayan with stunning success. Guided by Tulsidas’s sixteenth-century Rāmcaritmānas in Avadhi, immersed in the sociopolitical climate of Gandhi’s India, and shaped by technological advancements of the early twentieth century, Kathavachak composed his religious epic, the Radheshyam Ramayan, to update the story for modern audiences and to sing and explicate it in devotional concerts (kathā). To this end, he initially composed his Ramayan in the vernacular, a mixed register of Hindi-Urdu, a decision he would come to regret. Based on extensive literary, archival, and ethnographic research, and the observation of performances, The Radheshyam Ramayan in Text and Performance takes readers on a journey through Kathavachak’s hometown of Bareilly and his cosmopolitan world of Hindi letters and performance, unravelling the mysteries of why Kathavachak heavily revised his Ramayan over many years, how two other authors assisted him in writing it, and how the work quickly made its way onto Ramlila stages all over North India. The book also considers Kathavachak’s Ramayan in contrast to that of Tulsidas, and includes vignettes of actors who have recited verses from the Radheshyam Ramayan in the annual Ramlila festival. Ultimately, this book illustrates how Kathavachak contributed to the “epic modernity” of India.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.815
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.457
Teacher spread0.385 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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