Relevance of ELF speakers’ source speeches: interpreters’ interventions
Bibliographic record
Abstract
In their capacity as language experts, interpreters are sometimes expected to deliver target texts that are better than their underlying source text, especially when the latter was produced by a speaker in a language that is not their L1. The spread of global English has given rise to ever more occasions when interpreters encounter non-L1 speakers of English as a lingua franca (ELF). The question as to whether or not interpreters try to optimise those speakers’ input is addressed by applying Relevance Theory (RT) as a conceptual and methodological framework that helps to understand interpreters’ needs or readiness to augment relevance for their audience. The paper builds on data from the larger project CLINT (Cognitive Load in Interpreting and Translation). The 56 renditions by all 28 professional interpreters participating in the project’s interpreting part of two original ELF speaker texts and their edited versions are analysed with a view to the enrichment processes undertaken by the interpreters. A comparison of the renditions of the original versus edited versions of the two texts shows that interpreters do engage in such processes considerably more when rendering ELF texts, especially if they are technical in nature. Determining whether or not these interventions lead to actual cognitive effects in terms of information gains on the part of the audiences or to increased cognitive effort on the part of the interpreters requires additional comprehension testing and triangulation with other indicators of cognitive effort.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".