Personality Traits and Translation Quality: An Investigation of the Relationship in Iran Context
Bibliographic record
Abstract
Different translators with different personality traits make variant decisions in their translations. In this study, the effect of personality traits of Iranian translators on their performance quality was explored from a psychological perspective. In the first step, the BFI Test (Big Five-Factor Inventory) was administered to the 30 MA translation students in Tehran Islamic Azad University. In the second step, the researcher distributed two different English source texts among the participants for the purpose of translation. Having finished the task of translating, the target texts produced by the students were assessed to investigate the correlation between personality traits and the quality of the translation. Hence, three instructors of translation were recruited to evaluate the translations and correspondingly score them on the basis of Farahzad’s (1992). The analysis of the acquired results proved both of the hypotheses of the study. First, there was a positive relationship between personality traits and translators? performance quality in different text types and also, Psychological model of translators’ personality had a significant effect on assessing the translated works.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".