A Corpus-Driven Analysis of Explicitness in English as Lingua Franca
Bibliographic record
Abstract
This paper examines explicitness in English as lingua franca (ELF) spoken interactions. Using a conversationanalysis procedure, about 11h of audio-recorded naturally occuring ELF interactions of 79 incoming Erasmusstudents were analyzed for this purpose. The corpus was compiled by means of 54 speech events, 29 interviews and25 focus group meetings and the participants represented 24 mother tongues. Research into ELF reveals that ELFspeakers tend to use various “explicitness strategies” (Mauranen, 2007) in order to enhance intelligibility. Thefindings of this study show that there are indeed variations from standard ENL forms with respect to the degree ofexplicitness in spoken interactions. There is a tendency among ELF speakers to make the meaning more explicit forthe listeners. Repetitions of same expressions in subsequent sentences, use of over-explicit forms, use of an extrasubject following a relative clause and use of emphatic reference are the emerging patterns observed in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".