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A Moment of Truth in Translating Proper Names in Naguib Mahfouz’ Trilogy from Arabic into English

2012· article· en· W1833046155 on OpenAlexvenueno aff
Raghd Al Rabadi

Bibliographic record

VenueCross-cultural communication · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsTrilogyLiteratureTextualityFolkloreArtArabicLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

This study tackles the translation of proper names in Naguib Mahfouz’s Trilogy from Arabic into English. A masterpiece of three volumes, namely, Palace Walk, Palace of Desire, and Sugar Street, was translated by Hutchins and Kenny (1990), Hutchins, Kenny and Kenny (1991), and Hutchins and Saman (1992), respectively. In the English translation of this trilogy, proper names were preserved in a process of transliterating, thus maintaining a foreignized sense of rendition. Such mere strategy constitutes an alternative among a spectrum of many others suggested in the domain of translating proper names, viz., creation, adaptation, addition, omission, among others. Nevertheless, the researcher used four proper names as case studies representative of the inadequacy of merely transliterating proper names in Mahfouz’s literary work. Mahfouz imbued his work with an enchanting style that became an emblem of his folkloric locality. Yet this folkloric touch was not faithfully depicted in the English translation mainly due to the linguistic and cultural gaps between the source language and the target one. The analysis of the four names that the researcher purposefully chose represents such loss. A charge of functional equivalence and intended irony was traced thereby. Correspondingly, a backup strategy to compensate this inequivalence between the original work and its English rendition proves to be missing in doing justice to such work. Key words: Naguib Mahfouz; Literary translation; Foreignization; Domestication; Transliteration

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.066
GPT teacher head0.338
Teacher spread0.271 · 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.

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

Citations10
Published2012
Admission routes1
Has abstractyes

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