A Discussion of Color Metaphors From the Perspective of Cognition and Culture
Bibliographic record
Abstract
Metaphor is a pervasive phenomenon which has traditionally been considered as a figure of speech used for special effects in a speech or an essay. It is pervasive in everyday life. Color terms are usually used to depict the colors of objects in the world. Every object in the world has its own color. There are a large number of metaphorical expressions with color serving as the source domain in both English and Chinese. As a very important human experience, colors have attracted many scholars attention. The study revealed that possible reasons for the similarities of color metaphor in the two languages can be attributed to the common perceptual and cultural experience, while the dissimilarities originated from the different living environment, religion, custom, and philosophy etc. This essay makes a comparison of color metaphor about the similarities and differences between English and Chinese. Understanding similarities and differences of color metaphor between English and Chinese is of great importance in the cross-cultural communication. It’s beneficial for us to do English teaching, English translation, and appreciation of English culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".