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Record W3133373244 · doi:10.1515/asia-2020-0030

Indo-Persian narrative literature: Cultural translation and rewriting of Indian stories in Persianate South Asia

2020· article· en· W3133373244 on OpenAlexaff
Pegah Shahbaz

Bibliographic record

VenueAsiatische Studien – Études Asiatiques · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsSocial Sciences and Humanities Research CouncilUniversity of Toronto
Fundersnot available
KeywordsPersianNarrativeIslamPersian literatureLiteratureContext (archaeology)HistorySociologyLinguisticsArtPhilosophy

Abstract

fetched live from OpenAlex

Abstract The present article aims to study the translation and rewriting process of Indian narratives in Persian during the Delhi Sultanate (1206–1526) and the Mughal period (1526–1858), and to examine their cultural adaptations and strategies of adjustment to the Muslim recipient culture involving a reciprocal exchange of literary and cultural elements and religious interpretations. In the first stage, the features of Indo-Persian narrative tradition are briefly introduced with regards to structure and integral themes and in the second, the acculturation of Indian elements will be analysed according to Islamic principles and mystical thoughts in a selection of literary texts produced by Muslim Persian scholars. The article will focus on the representations of gender in stories and the perception of justice in the Perso-Islamic context to see, in particular, how narratives carried across Indian rituals and women’s codes of conduct to the Muslim readership; in other words, we try to shed light on how the alienated Indian became domesticated in the Persian-Muslim world of thought.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.325
Teacher spread0.277 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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