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Record W1987283218 · doi:10.5539/ass.v9n15p60

A Postcolonial Reading of Cecil Rajendra’s Selected Poems

2013· article· en· W1987283218 on OpenAlexvenueno aff
Gautaman Ganesan, Elangkeeran Sabapathy

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary Theory and Cultural Hermeneutics
Canadian institutionsnot available
Fundersnot available
KeywordsOrientalismPostcolonialism (international relations)Reading (process)ColonialismPoetryIdeologyContext (archaeology)Perspective (graphical)SociologyPoliticsPostcolonial literatureLiteratureGender studiesPolitical scienceHistoryLawArtVisual arts

Abstract

fetched live from OpenAlex

This study analyzes poems written by Cecil Rajendra, a Malaysian poet, from a postcolonial perspective. In other words, it entails a postcolonial reading of his selected poems. It seeks to identify and illustrate embedded concepts of postcolonialism and orientalism in the poems. It also seeks to understand how these concepts operate.The theoretical framework used in this research is the postcolonial theory, specifically employing Bill Ashcroft et al.’s Key Postcolonial Concepts and Edward Said’s Orientalism. It uses the Close Reading methodology to analyze the corpus, looking specifically into the concepts being used.The study reveals that although colonization has ended in many countries, a new form of colonization, namely Neo-colonialism is evident in many developing ex-colonies. This includes replication of colonial powers by today’s political leaders. It also speaks about Euro-centrism and its related ideologies which can operate oppressively against eastern nations.It is hoped that this study contributes to a better understanding about postcolonial issues, especially in the Malaysian context.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.006
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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