Comparison between Chinese and English Headlines and the Translation from English Headlines to Chinese Headlines
Bibliographic record
Abstract
By consulting different books and journals, this paper analyses the differences between Chinese and English headlines through the aspects of words, grammar, rhetoric and structure, then concludes methods to translate the English headlines to Chinese headlines. Key words: news headlines, literal translation, liberal translation Resume Par la consultation et la collection des documents, cet article analyse, sous les angles du vocabulaire, du mode, de la rhetorique et de la structure, les differences et les similarities entre les titres de nouvelle chinois et anglais, il conclut ensuite des problemes et des methodes en traduisant en chinois les titres de nouvelle anglais. Mots cles: les titres de nouvelle,la traduction litterale,la traduction libre 摘 要 本文通過查閱文獻和收集資料，從辭彙、時態、修辭和結構形式方面分析中英新聞標題的異同，然後總結出在把英語的新聞標題翻譯成漢語的標題時需要注意的問題和可以採取的方法。 關鍵詞：新聞標題；直譯；意譯
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".