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Record W4226255298 · doi:10.37867/te130410

CULTURAL AND TRADITION INFLUENCE OF TULSIDAS RAMCHARITMANAS IN FIJI

2021· article· en· W4226255298 on OpenAlexaboutno aff
Zafar khan A Pathan, Swati Kapadia

Bibliographic record

VenueTowards Excellence · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaHomelandEPICIdentity (music)Cultural identityHistoryEthnologyIndian literatureGeographyAncient historyAnthropologyGender studiesSociologySocial sciencePolitical scienceLiteratureArtLawAestheticsPolitics

Abstract

fetched live from OpenAlex

This article is based on the cultural and tradition influence of Tulsidas Ramcharitmanas in Fiji. In the nineteenth century, these indentured laborers who were separated from India left their country but retained their linguistic tradition and cultural wealth even in difficult times. The importance of cultural and influence of the epic Ramcharitmanas is spread in the world through various stories by the Indian Diaspora. The overseas Indians made the host-land their homeland, they had not only carried their Indian language but also Sanskari Gathari. Sanskari Gathari means Indian culture, rituals religious texts, etc. Indians in the host-land connect their story with Rama's exile; Indians as Indenture labourers or free migrants remember India every day in Fiji, Mauritius, Suriname, Trinidad, South Africa, Canada, etc. have their unique way to connect their identity to Lord Ram.

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)0.001
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.315
Teacher spread0.275 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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