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Record W2739211021 · doi:10.5539/ijel.v7n5p8

Discursive Strategies and Politics of (Neo-)colonialism: A Textual Analysis of Saadat Hassan Manto’s Letters to Uncle Sam

2017· article· en· W2739211021 on OpenAlexvenueno aff
Inayat Ullah, Iman Aib

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismDominance (genetics)GlobalizationPoliticsEconomic globalizationResistance (ecology)State (computer science)Power (physics)SociologyPolitical economyPhenomenonPolitical scienceGender studiesHistoryLawEpistemologyPhilosophyBiology

Abstract

fetched live from OpenAlex

Colonialism has been such a multifaceted and complicated phenomenon that it often juxtaposed the culture of the colonized in simultaneous assimilation of, and resistance to, the culture of the colonizers. Embedded in the theory of Post colonialism, this research aims at carrying out a qualitative analysis of discursive strategies used in Saadat Hassan Manto’s literary work Letters to Uncle Sam from a neo-colonial perspective. It seeks to highlight the issues of globalization and the effects that it engendered upon the then newly-established independent state of Pakistan. The research findings conclude that globalization has resulted in putting an end to the so-called purity of culture. Manto, therefore, explicitly satirizes the super power (read the United States of America) for its hidden agendas of manipulating and exploiting the economic system as well as the cultural beliefs of Pakistan under the mask of prospering nations by building a global market to create a new means of dominance that works through consent.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0120.015
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.349
Teacher spread0.324 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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