The effects of interpreters' accents on the perceived quality of English retour interpreting
Bibliographic record
Abstract
This thesis aims to investigate whether an interpreters' accent affects the perceived quality of retour interpretation. The theoretical part is divided into three thematic units: a chapter on retour, a phonetic chapter that addresses, among other things, the perception of non-native accents, and a chapter dealing with the issue of quality in interpreting. In the empirical part, our research aimed at answering the research questions is presented - three interpreters with different accents in English (Czech, American and British) interpreted three different speeches from Czech into English, after which respondents from a group of non- native speakers, native speakers of Canadian and American English and native speakers of British English were asked to evaluate the interpretations through scale questions that addressed several criteria to assess quality. Furthermore, all groups of respondents were asked to rate the three interpretations from best to worst. We found that accent played a role when respondents were forced to compare interpretations, but not in the case of individual evaluation of interpretations through scale questions. Fluency was found to be the biggest factor causing poorer ratings of the interpreting product. Keywords interpreting, simultaneous interpreting, native accent, non-native...
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".