Consecutive Interpretation: A Discourse Approach. Towards a Revision of Gile’s Effort Model
Bibliographic record
Abstract
In reality, expert interpreters from time to time do successfully tide over the gap between the capacity required and capacity available in dealing with extraordinarily large segments in consecutive interpretation. These exceptional cases imply that Gile’s Effort Model does not always hold and requires to be supplemented. This paper attempts to: 1) advance a solution to the dilemma that, in processing large segments in consecutive interpreting, the working memory capacity available is more often than not smaller than the capacity required, hence supplementing Gile’s Effort Model; 2) specify the rules of discourse transformation in consecutive interpretation; based upon the features of memory and consecutive interpretation, we deem that each segment, be it large or small, shall be processed as a discourse, the transformation of which is presumed to be the said solution; 3) and subsequently identify the optimal discourse transformation model, which is both capable of embodying the source text to the largest extent possible and achievable in terms of memory load. In addition, the author, through an observational study, justified the hypothesis. The validity of this theory, however, still requires further experimental evidence.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".