Bibliographic record
Abstract
Translating is a complex and fascinating task, as Richards (1965) once claimed that translating is probably the most complex type of event in the history of the cosmos. In the development of modern translation theories, there is a tendency that culture is introduced into this field. Translating becomes more complex for it has been defined as a cross-cultural communication event, and it involves not only two languages but also two cultures. This shift from emphasis on linguistic transfer towards emphasis on cultural transfer naturally exists in the translation of metaphor. Metaphor is not only an important figure of speech, but also a cognitive means of human mind. The people with different means of thinking have different cultures. The metaphorical language used by people must be fully saturated with culture peculiar to it. So because of the influence of cultural factor, the translation of metaphors becomes the most important particular problem. The paper discusses the reasons for the difficulties of metaphor translation, and summarizes several obstacles of it.
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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".