The Translator’s Subjectivity and Its Constraints in News Transediting: A Perspective of Reception Aesthetics
Bibliographic record
Abstract
Drawing upon the theory of reception aesthetics, this paper aims to systematically explore the news translator’s subjectivity and the constraints involved in transediting hard news texts. The translator, who actively receives, selects and conveys information, plays a decisive role in striking an appropriate balance between accuracy and acceptability during the transediting process. By optimally exerting his/her subjectivity, the translator can produce appropriate target news that communicate effectively. Interest in the translator’s subjectivity has continued to grow since the cultural turn in translation studies which introduced a range of new approaches. These either focus exclusively on the translator’s subjectivity or emphasize the constraints impinging upon it, but, in either instance fail to provide a comprehensive account. In contrast, the theory of reception aesthetics, which takes both aspects into account, provides a more thorough theoretical framework. This paper first performs a theoretical analysis of the reciprocity between the translator’s subjectivity and its corresponding constraints. A case study on English-Chinese news transediting in the Taiwanese press is then conducted to further explain how to apply the theoretical analysis in order to examine and assess the translator’s constrained subjectivity in actual transediting practice.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".