Comparative Contents of Idioms with Comparisons in English and Vietnamese from a Cognitive View: A Contrastive Analysis
Bibliographic record
Abstract
Idioms with comparisons are common in both English and Vietnamese. These idioms are formed by three component parts, namely comparative contents, comparative connectors, and comparative conventional images (Giang, 2023). This article uncovers the comparative contents of idioms with comparisons in English and Vietnamese in the light of cognitive linguistics through idiom analysability (Langlotz, 2006). This contrastive analysis presents the similarities and differences between English and Vietnamese idioms with comparisons in terms of their comparative contents, including closed comparative contents and open comparative contents. A manual search of Giang's (2018) idiom collection helped to establish a corpus of 672 English and 731 Vietnamese idioms with comparisons, which served as the data for the research. The results of this study show that the most significant difference between English and Vietnamese idioms with comparisons lies in the distribution of categories of comparative contents. The closed comparative contents of idioms with comparisons in Vietnamese are more prevalent than those in English, and vice versa for open comparative contents. The average proportion of closed comparative contents through idioms with comparisons in English is slightly higher than that of those in Vietnamese. The open comparative contents can be possibly and impossibly explicit; however, the possibly explicit open comparative contents of idioms with comparisons are unique in Vietnamese.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".