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Record W4229066194 · doi:10.29173/jjs67

Samhita Arni's Daring Debut

2022· article· en· W4229066194 on OpenAlexvenueno aff
Felicity Hand

Bibliographic record

VenueJournal of Juvenilia Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicIndian History and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsLiteratureSanskritPoetryNarrativeEPICOralityHistoryChoseArtSociologyLawLiteracy

Abstract

fetched live from OpenAlex

Indian writer Samhita Arni started writing and illustrating her first book when she was just eight years old. The really outstanding feat of this child sensation is that Arni chose to rewrite the Mahabharata, one of the two Sanskrit epics of ancient India, a narrative poem composed around 400 years BCE and still a highly significant cultural icon in contemporary India. Indian children are brought up on stories from the Mahabharata and the Ramayana from a very early age so the characters and their values become role models for boys and girls alike. The Mahabharata – A Child’s View (published in 1996 when Arni was eleven) takes on a critical stance against one of the epic’s major themes: war and she seeks to give a voice to many of the underestimated characters, especially women and members of the lower classes. 
 Arni’s naivety in embarking on such a momentous task as a contemporary rewriting of a classical work needs to be remarked on. The Mahabharata is possibly the world’s longest known epic poem, one of its many versions consisting of well over 200,000 lines. She has deliberately omitted certain episodes and daringly added new ones without deviating from the overall plot. Her fearless feminist stance, striking for a girl of eleven, proves that valid contemporary readings of the classics indicate their timeless quality and the essential orality of these texts, without which they can too easily be fossilized and rendered irrelevant for modern times. I conclude by suggesting that Arni’s debut novel is a challenging dialogue between ancient ideals and twenty-first century social and political issues.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.268
Teacher spread0.164 · 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.

Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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