Bibliographic record
Abstract
Although the use of English borrowings in Chinese is not uncommon, there are still few studies having explored the pragmatic dimension of these borrowings. Besides, few of the previous studies adopt an onomasiological approach to study borrowings. Thus, this study aims to investigate the pragmatic functions of English borrowings by comparing English borrowings with their native equivalents in line with the onomasiological approach. To this end, the study collects data from Weibo and divides the English borrowings occurring in the hot topics on Weibo into two types based on the existence of native equivalents, namely, catachrestic borrowings and non-catachrestic borrowings. It is revealed in the study that catachrestic borrowings provide a stereotypical interpretation of a concept that has not been lexicalized in Chinese and primarily serve the function of filling lexical gaps while non-catachrestic borrowings can create special pragmatic effects as marked choices in contrast to their native equivalents. While drawing the distinction between catachrestic and non-catachrestic borrowings helps shed light on their different functions, this study argues that the distinction is dynamic and context-dependent as the pragmatic functions of borrowings are conditioned by contexts and susceptible to change. By analyzing borrowings from the pragmatic dimension, this study not only demonstrates the importance of pragmatic functions in people’s adoption of borrowings but also confirms the applicability and effectiveness of an onomasiological approach to borrowing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".