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Record W4413877011 · doi:10.24113/smji.v13i8.11586

Tracing the Aspects of Postmillennial Indian Fiction: A Study of Laburnum for My Head by Temsula Ao

2025· article· en· W4413877011 on OpenAlexaboutno aff
K. Subapriya

Bibliographic record

VenueSMART MOVES JOURNAL IJELLH · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicIndian History and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsHead (geology)TracingArtHistoryComputer scienceBiologyProgramming languagePaleontology

Abstract

fetched live from OpenAlex

The experience of the tribes can never be labelled under one umbrella. It varies between every region and country. G.N. Devy in his Introduction to Indigenity: Culture and Representation states that tribes are "recognized as "Aborigines" in Australia, as Maori in New Zealand, as "First Nations" in Canada, as "Indigenous" in the United States, as "Janajatis" in India…as "Adivasis" in the terminology of Asian Activists" (XI). The names and terms alone not vary but each tribe as mentioned by the anthropologists has unique practices, culture and belief. For all these tribes, oral literature is the mother of all forms of literature. However, in the modern era, the survival of tribes is possible only with their written expression which is mandatory to sustain with the mainstream literature. This article aims to trace the text Laburnum for my Head as a postmillennial Indian fiction that presents the transformation of tribal literature from oral to written form addressing their contemporary issues. The form and content of the tribal literature has varied according to the context of the modern era. These issues addressed by Temsula Ao also covers the subaltern aspects like the role of tribal women and the portrayal of tribes as Naxals in the North East India. This text by various means serves as an appropriate example for a postmillennial Indian fiction.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.636

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)0.0000.000

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 head0.023
GPT teacher head0.253
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 teacher head, 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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