Problems in Translating Culture: The Translated Titles of Fusheng Liuji1
Bibliographic record
Abstract
Translating culture poses fundamental problems of perception and conception far deeper than matters of linguistic expression. This essay explores some of these problems by examiningFusheng liuji(Six Records of a Floating Life), a Chinese autobiographical text that has been translated into fourteen Asian and European languages. Even without going into the details of the rendered versions, one can notice various forms of intercultural mediation and reshaping in the translated titles and added subtitles. At one end is direct, partly helpless substitution: lexically flawless “float” cannot encompass the rich matrix of philosophical connotations and artistic resonances offuin the source culture. At the other end is active reshaping: recasting, addition and omission based on interpretive (mis)reading, including a reduction of imagistic language into abstract concept (e.g.,fubecomes “fleeting”). Through examining 17 renditions of the title ofFusheng liuji, this essay offers a case study that helps to cast light on the unavoidable factor of intercultural mediation in the translation process, with special focus on the translation of philosophical and aesthetic concepts. Some forms of mediation carry more significant effects than others, and there may be differences in verbal resources and orientations in various languages worthy of notice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".