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Record W7160980422 · doi:10.46568/pjass.v15i1.768

A Comparative Analysis of Rudyard Kipling and Lord Byron’s Treatment of the Orient: Application of Edward Said’s Theory of Orientalism

2024· article· W7160980422 on OpenAlexaff
Dur-e-Shahwar Ali, Sahar Afshan, Panira Ali

Bibliographic record

VenuePakistan Journal of Applied Social Sciences · 2024
Typearticle
Language
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsOrientalismIdeologyGeniusTheme (computing)HegemonyPoetryLiterary criticismRomance

Abstract

fetched live from OpenAlex

This study aims to provide a comparative analysis of Lord Byron’s and Rudyard Kipling’s literary works. The study postulates the unique treatment of the Orient, which is idiosyncratic to the formerly mentioned writers. In their manipulation of the Orient for their own literary and cultural purposes, in their representation of the West as a superior geo-cultural reality over, Byron and Kipling employ images and rhetorical strategies which ensure their participation in an intertextual process of literary Orientalism. The research seeks to investigate their literary works displaying the influence and implications of socio-political relations between the West and the East. This study further analyzes how the hegemonic discourse, or the ideology of a dominant society, has essential, fixed, and divided identities through the construction of binary divisions of Western ideology as civilized and Others as savages. Edward Said’s theory of Orientalism (1978) is used as a lens to compare both writers. The research concludes that Orientalism is a major theme of Byron’s literary works; however, some critical romantic elements of Byron’s poetry overshadow the elements of Orientalism. On the contrary, Kipling’s sense of imperialism overwhelmed his poetic genius throughout his literary career; therefore, he is better known as an Orientalist in a true sense of the word.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
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 head0.031
GPT teacher head0.320
Teacher spread0.289 · 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.

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
Published2024
Admission routes1
Has abstractyes

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