Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".