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Rewriting The Past: Historiographic Metafiction In Anita Rau Badami’s Can You Hear The Nightbird Call

2025· article· W7160269156 on OpenAlexaboutno aff
K Camalame

Bibliographic record

VenueShanlax International Journal of Arts Science and Humanities · 2025
Typearticle
Language
FieldArts and Humanities
TopicSouth Asian Studies and Diaspora
Canadian institutionsnot available
Fundersnot available
KeywordsHistoriographyNarrativeEmbodied cognitionMetafictionMode (computer interface)Perspective (graphical)Rewriting

Abstract

fetched live from OpenAlex

This Paper examines Anita Rau Badami’s Can You Hear the Nightbird Call as a work of Historiographic metafiction that interrogates the subtle narratives of partition, 1984 anti-Sikh riots and Indo-Canadian experience. A novel apart from being a representational genre also is an effective and powerful mode of discourse. Negotiations between history and novel are more frequent as compared with other modes of fiction. Novel has become an important medium of problematizing and questioning to a certain extent the discourse of history. . In historical fiction, writers attempted to write undisclosed and concealed chapters of Indian history keeping an alternative perspective towards history with a direct reference to politics, state and nation, Based on Linda Hutcheon ‘s concept of historiographic metafiction, the paper argues that the novel does not merely recount of historical events but subtly interrogates how histories are constructed, remembered and embodied thereby positioning memory as a critical tool for reimagining historiography through the principal characters like Bibi-ji, Leela and Nimmo. Badami thereby foregrounds how the past operates both as a site of trauma and also positioning memory as vital element for reimagining historiography.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.017
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.260
Teacher spread0.230 · 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 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
Published2025
Admission routes1
Has abstractyes

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