Dissenting Voices: When Paratexts Clash With Texts. Paratextual Intervention in Persian Translations of Texts Relating to the Iran-Iraq War
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".