Bibliographic record
Abstract
This chapter provides a review of studies on working memory (WM) and interpreting between the 1970s and 2010s, with special attention paid to simultaneous interpreting (SI) and consecutive interpreting (CI). Previous research has investigated three major issues: (1) the interpreter’s advantage over noninterpreters in WM capacity and executive control; (2) the relationship between overall WM capacity, WM executive control and interpreting performance, and (3) the interaction that takes place between long-term memory and WM to facilitate meaning retrieval from the source language, interlingual reformulation, and message delivery into the target language. This chapter will first review major WM models of interpreting to determine what SI and CI have in common and how they differ in processing routes; secondly by examining relevant empirical evidence that (in)validates such models, and thirdly by proposing new possibilities for research on WM in both SI and CI. By means of a synthesized review and an in-depth comparative analysis, this chapter will shed new light on how WM demand differs across interpreting tasks and fluctuates during the interpreting process, which will in turn contribute to future interpreting research and pedagogy.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".