An Introduction to the Ambiguity Tolerance: As a Source of Variation in English-Persian Translation
Bibliographic record
Abstract
Different individuals provide different translations of different qualities of the same text. This may be due to one’s dominant cognitive style and individuals’ particular personal characteristics (Khoshsima & Hashemi Toroujeni, 2017) in general or ambiguity tolerance in particular. A certain degree of ambiguity tolerance (henceforth AI) has been found to facilitate language learning (Chapelle, 1983; Ehrman, 1999; Ely, 1995). However, this influential factor has been largely overlooked in translation studies. The purpose of this study was to find the relationship between AT and translation quality by identifying the expected positive correlation between the level of AT and the numbers of translation errors. Out of the 56 undergraduates of English-Persian Translation at Chabahar Maritime University (CMU), a sample of 34 top students was selected based on their scores on the reading comprehension which enjoys a special focus in many contexts (Khoshsima & Rezaeian Tiyar, 2014) and structure subtests of the TOEFL. The participants responded to the SLTAS questionnaire for AT developed by Ely (1995). The questionnaire had a high alpha internal consistency reliability of .84 and standardized item alpha of .84. In the next stage of the research, the participants translated a short passage of contemporary English into Persian, which was assessed using the SICAL III scale for TQA developed and used by Canadian Government’s Translation Bureau as its official TQA model (Williams, 1989). Then, to find the relationship between the level of ambiguity tolerance in undergraduates of English-Persian translation at Chabahar Maritime University and their translation quality, analysis of the collected data revealed a significant positive correlation (r=440, p<.05) between the participants’ degree of AT and the numbers of errors in their translations. Controlling for SL proficiency, the correlation was still significantly positive (r=.397, p<.05). Accordingly, it was concluded that the more intolerant of ambiguity a person is, the more errors s/he is likely to make while translating; conversely, the more tolerant of ambiguity a person is, the higher the quality of his/her translation will be. Therefore as expected, analysis of the data revealed a positive correlation throughout the sample between ambiguity intolerance and translation quality.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".