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Record W2990298709 · doi:10.7202/1065330ar

Dissenting Voices: When Paratexts Clash With Texts. Paratextual Intervention in Persian Translations of Texts Relating to the Iran-Iraq War

2019· article· en· W2990298709 on OpenAlexvenueno aff
Reza Yalsharzeh, Hossein Barati, Akbar Hesabi

Bibliographic record

VenueMeta Journal des traducteurs · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativePersianFraming (construction)ParatextLiteraturePoliticsMetadiscourseLinguisticsHistorySociologyArtPolitical sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

This study uses narrative theory and the concept of narrative framing, as elaborated by Somers and Gibson (1994) and Baker (2006), to study the paratextual mediation and discursive presence of different agents in Persian translations of political texts written by Western authors about the Iran-Iraq war. By exploring the narratives dominant in the paratexts, and more specifically in the prefaces and footnotes of Persian translations, this paper examines how these have played a crucial role in reframing the narratives of Western authors through paratextual material. To this end, the narratives in the paratexts have been analyzed using four framing techniques elaborated by Somers and Gibson (1994) and Baker (2006), which are framing by temporality/spatiality, selective appropriation, labeling and participant positioning. The analysis of paratextual material shows that, apart from their introductory and explanatory functions, paratexts can be viewed as a kind of metadiscourse on the actual translations. The paper concludes that paratexts in the Persian translations are used in political and ideological ways to guide target language readers and to express the appropriate interpretations, or deemed appropriate, by the various institutional participants involved in the translation process in Iran.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.285
Teacher spread0.222 · 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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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