Relevance, Processing Effort, and Contextual Effect in Farsi Translation of Joyce’s A Portrait of the Artist as a Young Man
Bibliographic record
Abstract
Relevance Theory of Communication has various implications in translation. Since translation can be considered as expression and recognition of intentions, and based on the presumption that a large number of Farsi translations are not successful in this process, this study aimed at detecting the obstacles which hinder this process and effective procedures in avoiding and overcoming such obstacles. To this end, five sample paragraphs were selected from Joyce’s A Portrait of the Artist as a Young Man and two translations of it into Farsi. Then five instructors of translation studies were asked to evaluate the selected texts and list the factors which increased processing effort based on the guidelines adopted from principles of Relevance Theory and the criteria proposed by vandijk (1979). The results indicated that there were some factors that decreased the relevance level of the text for the Persian audience. Taking the raters’ notes into consideration and analyzing the texts of each translation, the researcher came up with some helpful guidelines to avoid such obstacles in the process of translation e.g. avoiding uncommon phonological patterns or concepts which are unknown to the Persian audience. Key words : Translation; Relevance; Processing effort; Contextual effect; Communication
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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 | 0.004 | 0.035 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".