Relevance-theoretic Interpretation of Soft News Translation and its Implications for Translation Teaching
Bibliographic record
Abstract
Abstract: The author gives a thorough interpretation of the translation process and methods under the framework of relevance theory based on the actual pairs of Chinese soft news and the counterpart English versions well selected from authoritative bilingual magazines. The author attests the explanatory force of relevance theory to soft news translation and also summarizes the implications for teaching. Key words: Relevance theory; Soft news translation; Teaching; Communication Resume: L'auteur donne une explication approfondie du processus de traduction et des methodes dans le cadre de la theorie de la pertinence sur la base des nouvelles legeres chinoises et leurs versions en anglais bien choisies dans des magazines bilingues connus. L'auteur atteste la force explicative de la theorie de la pertinence dans la traduction des nouvelles legeres et resume egalement les implications pour l'enseignement. Mots cles: Theorie de la pertinence; Traduction des nouvelles legeres; Enseignement; Communication
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".