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Record W2119939910 · doi:10.5539/ells.v5n1p21

Bringing ‘India of Darkness’ into Light: A Socio-political Study of Aravind Adiga’s The White Tiger

2015· article· en· W2119939910 on OpenAlexvenueno aff
Monir A. Choudhury

Bibliographic record

VenueEnglish Language and Literature Studies · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicIndian History and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsTigerWhite (mutation)PoliticsPower (physics)Economic JusticeMedia studiesSociologyGender studiesPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

This study has investigated how Aravind Adiga’s Man Booker Prize winner debut novel The White Tiger (2008) has protested against the popular image of a shiny India. This inquiry is important because it has identified how the politicians have deceived the people of India by creating a false sense of development and advancement. The focus of this study is to demonstrate Aravind Adiga’s The White Tiger as a socio-political critic of modern India. It has contributed in interpreting the life and culture of the people both in rural and urban India through analyzing some of the most powerful metaphors of the text. The paper has identified a number of ways how the rich has been exploiting the poor over centuries and how the basic needs of the poor have been ignored by the politicians in power. It is hoped from the current study that the writers, journalists, columnist and literary critics will raise the issues of the socio-politically suppressed people to bring justice to them.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0320.026
Scholarly communication0.0140.006
Open science0.0020.005
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.252
Teacher spread0.232 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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