Bibliographic record
Abstract
espanolBharati Mukherjee, autora de origen indio de doble nacionalidad canadiense y estadounidense, nos ofrece en sus novelas un universo femenino entre la India y America, los mitos griegos e hindues. Identifica las trayectorias vengadoras de sus heroinas con la de Electra, matricida, y la de Kali, la diosa hindu de la guerra de multiples brazos. Asi, trabajando sobre la nocion griega de destino y la hindu del Dhanna, Mukherjee entreteje hilos griegos e hindues que pertenecieron sin duda en otro tiempo a tramas mitologicas comunes. Ademas, inserta en su obra enlaces biblicos y shakespearianos, provocando en el lector un vertigo intercultural. EnglishBharati Mukherjee is an American and Canadian writer of lndian descent. Some of her stories are set both in India and in America, thus allowing her female characters to be identified with Western and Eastern mythological heroines, or more precisely with Greek and Hindu mythological heroines such as Electra and Kali. Thus, the author manages to weave together Greek and Hindu mythological threads that certainly used to belong to a common mythological fabric thousands of centuries ago. As a result, she works both on the motif of Greek Fate, as well as on the Hindu notion of Dhanna, allowing the reader to ponder over the similarities between Greek and Hindu myths. Resides, she also weaves Biblical and Shakespearian threads into her narrative, causing a feeling of vertigo in the reader.
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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 | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".