Bibliographic record
Abstract
Transliteration has played an important role in lexical borrowing from foreign languages into Chinese. In this paper the question of lexical borrowing from English into Chinese via transliteration is treated from multiple perspectives with data drawn from A Dictionary of Loan Words and Hybrid Words in Chinese, the most authoritative dictionary of loanwords in Chinese so far. It is found that as a method of adaptation, transliteration is used in three ways, namely phonetic transcription, transliteration plus notes, and half transliteration plus half translation, which bring into being three subtypes of transliterations respectively: phonemic loans, annotated transliterations, and loanblends. Three strategies have been adopted to add semantic transparency to transliterations: direct labeling of semantic category with radicals or characters, indirect suggestion of meaning by combining characters in syntagmatic lexical relations conforming to Chinese word-formation processes, and addition of meaning through endowing transliterations with positive, negative, or jocular connotations. An important means to enrich the Chinese lexicon and promote products in advertising language as it is, transliteration poses problems of understanding, including distortion of meaning and folk-etymological interpretation.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".