On Phatic Communion Translation in Subtitle from the Perspective of Politeness Principles: A Case Study of the Chinese Subtitle of American TV Series Why Women Kill
Bibliographic record
Abstract
Phatic communion conversations are culturally-constrained speech acts happening every day and everywhere. Different cultural backgrounds between China and America will lead to the different applications of phatic communion. Considering American TV series as a mass media for cultural exchanges, subtitle plays a vital role in bridging two cultures. This paper introduces the related researches at home and abroad on phatic communion, summarizes the main methods adopted in translating different types of phatic communion by analyzing all the collected phatic communion conversations in Why Women Kill . The investigation of whether the subtitled phatic communion achieving the same illocutionary meaning that the original speakers trying to convey under the guide of politeness principles has been completed. The conclusion can be drawn as follows: the translator tends to apply different strategies in translating different types of phatic communion such as literal translation, paraphrase, cultural substitution, addition, condensation and specification methods to achieve the pragmatic equivalence between the original and the subtitle.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".