An Eco-translatology approach to investigating the translation of comics: Case study of The Sayings of Zhuangzi
Bibliographic record
Abstract
Featuring the interplay of verbal and visual content, the translation of comics has received theorists’ attention in recent decades. To shed new light, this study analysed the translation of comics from a new perspective, namely Eco-translatology. Selecting 自然的簫聲：莊子說 [The Sayings of Zhuangzi: The Music of Nature] and its English translation, The Sayings of Zhuangzi: The Music of Nature, as the research texts, this study conducted an in-depth analysis and obtained three major findings: (1) the translators have professional knowledge and expertise to process the comic book appropriately, (2) the target ecosystem shares a similar composition and creates the same effect as the source at the micro- and meso-levels and (3) the target ecosystem is constructed in a composition that attains harmony and interaction at the macro-level. Amplification, reduction/omission and literal translation are the three most-adopted coping strategies to achieve linguistic and cultural transformation, whereas the inclusion of an introduction and the use of footnotes fulfill communicative transformation. This paper argues that translators attach the utmost importance to the “comprehension of stories,” followed by “space constraints,” “fluency of translation,” and “faithfulness to the original meaning.”
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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 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".