Bibliographic record
Abstract
This article analyses consecutive and simultaneous interpreting from a strategic point of view. A concept of discourse-based mental modelling serves as a basis to describe the cognitive-linguistic processes and strategies underlying comprehension and production in interpreted bilingual communication. By contrast with processing in the monolingual context, the interpreting process is characterised by adverse conditions including above all the lack of semantic autonomy and the continuing presence of elements of the source language during different stages of processing. The strategies interpreters use will therefore necessarily differ from those used in monolingual communication and will be adapted to the specific requirements of the interpreting process. Characteristic difficulties of interpreting and corresponding strategic processes are discussed with reference to the communicative transfer relation between source and target discourse, the sequential organisation of source discourse comprehension and target discourse production in consecutive interpreting, the parallel organisation of source discourse comprehension and target discourse production in simultaneous interpreting, and the complexity of content andlor linguistic representation of the source discourse. In an empirical analysis, a bit of simultaneous discourse is studied along with introspective data on the processes at work during production. The introspective data was obtained by means of a retrospective thinking-aloud protocol. Product-related and process-related data indicate that the strategies discussed are used in real-life interpreting situations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".