Strategic syntactic restructuring during simultaneous interpreting from Turkish into English
Bibliographic record
Abstract
This study investigated the effects of contextual constraint and transitional probability on verb interpreting latency and syntactic restructuring during simultaneous interpreting from Turkish verb-final into English (verb-medial) sentences by trainee and professional Turkish (A)—English (B) interpreters. We found that contextual constraint, but not transitional probability, leads to both a decrease in interpreting latency on the sentence-final verb and a higher degree of syntactic restructuring between the source language input and target language output in both trainee and professional interpreters. Moreover, no between-group differences were observed in the effect of contextual constraint on verb interpreting latency and syntactic restructuring. The present findings suggest that, irrespective of experience, interpreters use contextual cues to restructure the word order between the source input and target output and produce the verb faster in the target output. This provides an argument for examining interpreting latency and syntactic restructuring together, as possible indicators of unvoiced anticipation during simultaneous interpreting between languages with dissimilar structures, such as Turkish and English.
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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 | 0.001 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".