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Record W3005683023 · doi:10.5539/ijel.v10n2p284

A Sociopragmatic Analysis of Women and Gender Roles in John Galsworthy’s Forsyte Saga and Naguib Mahfouz’s Cairo Trilogy

2020· article· en· W3005683023 on OpenAlexvenueno aff
Abdulfattah Omar, Musa Ahmed Musa Alhassan

Bibliographic record

VenueInternational Journal of English Linguistics · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
FundersPrince Sattam bin Abdulaziz University
KeywordsTrilogySalience (neuroscience)NarrativePerspective (graphical)Style (visual arts)SociologyPower (physics)PoliticsLinguisticsLiteraturePsychologyPolitical scienceComputer scienceArtPhilosophyLaw

Abstract

fetched live from OpenAlex

This study is concerned with investigating the treatment of women and gender roles in Glasworthy’s Forsyte Saga and Naguib Mahfouz’s Cairo Trilogy from a sociopragmatic perspective. The texts studied for this paper have not been evaluated to socio-pragmatic analysis that reflects the little application of this approach to literary works. As thus, the goal of this paper is to advance sociopragmatic analysis to these novels—there is salience from the style, narrative techniques, and language utilized by both writers in their books, which indeed points to pragmatic undercurrents that must be explored. The results indicate that social and political aspects are key elements for understanding women and gender issues in the selected texts. The integration of these contextual elements revealed how the two authors manipulated literary discourse to reflect on the power relations and struggles between men and women of their age. It can be claimed that sociopragmatic approaches provide opportunities for understanding the hidden layers within the selected texts in terms of social practices and interactions among characters. It is finally suggested that sociopragmatic approaches should be integrated into literary studies for a better and deeper understanding of literary discourse in general and crosscultural issues in particular.

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)0.000
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.030
GPT teacher head0.287
Teacher spread0.257 · 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
Published2020
Admission routes1
Has abstractyes

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