A Descriptive Study of Norms in Interpreting: Based on the Chinese-English Consecutive Interpreting Corpus of Chinese Premier Press Conferences
Bibliographic record
Abstract
Interpreting performance is shaped by three major forces: a) the interpreter’s interpreting competence, b) cognitive conditions on-site and c) norms of interpreting. This research is a descriptive study of norms in the Chinese-English interpreting of Chinese Premier Press Conferences, which reveals the actual norms of consecutive interpreting especially with regard to source text and target text relations. It employs the research paradigm of descriptive translation studies and the analytic tool of shifts. Through inter-textual comparative analysis of the parallel corpus of the on-site interpretation of 11 Chinese Premier Press Conferences (1998-2008), three types of shifts are identified, including Type A shifts (Addition), Type R shifts (Reduction) and Type C’ shifts (Correction). With quantitative statistics of the regularity of the occurrences of shifts and qualitative analysis of every type of shifts in the corpus, four typical norms of ST-TT relations are identified: a) the norm of adequacy, b) the norm of explicitation in logic relations, c) the norm of specificity in information content, d) the norm of explicitness in meaning. This descriptive study of norms based on a relatively large corpus of on-site interpretation can serve as a tentative exploration of the methodology in descriptive interpreting studies. It may also shed new light on interpreting quality studies.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".