Strategies in a corpus of simultaneous interpreting. Effects of directionality, phraseological richness, and position in speech event
Bibliographic record
Abstract
The study surveys the existing literature on strategies in simultaneous interpreting (understood here as transformations indicative of interpreting procedures that manifest in the product of interpreting). On the basis of the survey, a summary of eight strategies which are present in various research strands is compiled. I use a parallel bidirectional corpus of Ru-En simultaneous interpreting to extract a random sample of 360 fragments and investigate the presence of the eight strategies in the sample. The type of strategy is then correlated with three variables: direction of interpreting, position of the source fragment in the original text, and phraseological richness of the source fragment. The findings indicate that all the strategies, including an additional transformation category (incorrect interpretations), are present in the sample, although some of them are considerably less common than earlier literature purports. All three variables have significant association with the type of strategy, although in cases of directionality this holds only for saucissonnage and omission. A close analysis of three coding categories—omission, explicitation, and incorrect interpretations—suggests that interpreters in this corpus orient more towards a performative than informative function of their SI.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".