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Record W2606074674 · doi:10.3968/9122

The “Writing Back Paradigm” and the Relevance of Postcolonial Stylistics

2017· article· en· W2606074674 on OpenAlexvenueno aff
R. Kalpana

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOptimal distinctiveness theoryStylisticsContext (archaeology)Relevance (law)AppropriationSociologyLiteratureAestheticsLinguisticsHistoryPhilosophyPsychologyArtPolitical scienceLawSocial psychology

Abstract

fetched live from OpenAlex

Postcolonial writers writing in English resorted to various strategies and deviations to maintain the cultural distinctiveness and nativity of both text and context. This practice is also true of postcolonial writers in the postliberalisation era. These strategies and deviations, as a process of “acculturisation”, have been described by the use of terms such as “abrogation” and “appropriation” by Ashcroft et al. (1989). This article collects examples from the works of various postcolonial writers as reviewed by various critics and analyses various strategies, deviations and innovations attempted by these writers to mark their distinctiveness.

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)0.012
metaresearch head score (Gemma)0.013
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.004
Science and technology studies0.0090.079
Scholarly communication0.0140.009
Open science0.0020.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.288
Teacher spread0.267 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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