English as a lingua franca-induced effects on cognitive load and interpreting quality
Bibliographic record
Abstract
Over the last decades, English has become the unchallenged lingua franca at international gatherings. English as a lingua franca (ELF) is not without consequences for interpreters in that they increasingly face the difficult task of having to interpret non-native speakers. Much of the ELF-related research in interpreting studies has so far focused on non-native accented speech. Current surveys among interpreters suggest, however, that accent is not the only difficulty. Instead, it seems that non-native speeches are characterized by a wide range of phenomena such as lack of cohesion and unclear argumentation, lexical and grammatical irregularities, increased explicitness and signs of processing, all of which may contribute to adding to the interpreters’ cognitive burden. In the SNSF-funded CLINT-project, we are currently addressing this research gap. Ongoing data collection allows us to look at the simultaneous English to German interpretations of 20 professional interpreters for the investigation of the effect of ELF on interpreting. The authentic source speech delivered at a conference on energy-related matters was produced by a non-native English speaker. It was recorded, transcribed and re-spoken by a Canadian native speaker to control for accent. In-depth analysis confirmed that it contained a considerable number of phenomena typical of ELF-speeches, which may affect interpreters’ cognitive load. Interpreters were also presented with a second version of the same speech which was edited to comply with standard British English. Ten participants interpreted the original ELF-version of the speech, the other ten participants interpreted the edited version of the same speech. Based on the assumption of higher cognitive load involved in interpreting an ELF speech, we expected to find an overall lower interpreting quality as well as an effect of fatigue, reflected in an earlier decline in interpreting quality. Interpreting quality is a multifaceted concept, a major aspect of which is accuracy or, more precisely, completeness and sense consistency with the source text. At the HKBU Conference we propose to present a promising method for the quantitative assessment of sense consistency and completeness of a target text over time. The newly designed method has been used on the first set of data, or first 20 interpretations, procured as part of the above-mentioned project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".