Logicality in Text Translation LOGIQUE DANS LA TRADUCTION DU TEXTE
Bibliographic record
Abstract
Similarities and distinctions in logical structures of the Chinese and English languages are due to different but interrelated logical systems of China and the West. We may well say Logics influences translation and monitors the whole process of translation. In order to make translation more suitable to the target language's logical system, this paper views from the aspect of logic and discusses some practical and feasible logical translation methods by analyzing examples. The paper puts forward some suggestions on logical text translation. 1) When translating texts showing the three universal laws of Logics, i.e. the law of identity, the law of non-contradiction and the law of excluded middle, and the law of space, translators should translate them according to the logical sequence of the source language. Chinese and English are different in leading inferential modes, therefore, translators should reorganize the logical sequence according to the phenomena that Chinese focus on induction and English focus on deduction. 2) When translating texts showing the law of time and the law of cause and effect, translators should also reorganize the logical sequence according to the target language's features.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".