Discourse Markers in English as a Target Language: The Use of so by Simultaneous Interpreters
Bibliographic record
Abstract
This paper investigates the distribution of a particular discourse marker, i.e. so, in the target speeches produced by professional simultaneous interpreters while translating from Italian into English. The objective is to examine the possible effect on discourse marker distribution of specific situational norms that are in play in simultaneous interpreter-mediated settings. The analysis is both quantitative and qualitative, and is based on a parallel corpus of three medical conferences with Italian and English (native and non-native) speakers along with the corresponding simultaneous interpretations. All the occurrences of zero correspondence (30% of all the occurrences of so in target speeches) are examined in detail and grouped into different macro-categories. Subsequently, there is a discussion of possible reasons behind the interpreters' decision to add "sequentially dependent elements which bracket units of talk" (Schiffrin 1987: 31), with a view to contributing to the description of English in interpreter-mediated communication.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".