Explicitation of Conjunctive Relations in Ghabraei’s Persian Translation of ‘The Kite Runner’
Bibliographic record
Abstract
Explicitation has been one of the most important topics in recent translation studies. This research sought to investigate explicitation as a translation universal based on a contrastive analysis between Persian and English languages. The main focus of the study was to confirm the process of explicitation and to investigate the explicitation devices adopted in the Persian translation of the conjunctions between sentences and clauses in an English text. For the purposes of this research, one-third of a novel, namely The Kite Runner written by Khaled Hosseini (2003) and its Persian translation by Mehdi Ghabraei (2006) were scrutinized for any occurrence of shifts of conjunctions. The aim of the research was to test the explicitation hypothesis according to Blum-Kulka (1986) and the model which was followed for the analysis of conjunctive relations was that of Halliday and Hasan (1976). The results of the investigation indicated that the processes of explicitation, implicitation, and also the meaning change were observed in the corpus, although explicitation took a bigger portion in the target text. The analysis of the explicitated conjunctive relations indicated that two devices had been adopted by the translator, namely the addition of conjunctions and replacing punctuation marks with conjunctions. Furthermore, it was found that the translator had explicitated all four types of conjunctive relations, i.e. the additive, adversative, causal and temporal relations; however, from among these conjunctive relations, temporal ones were more explicitly portrayed in the target text.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".