Cognitive Analysis of Chinese-English Metaphors of Animal and Human Body Part Words
Bibliographic record
Abstract
Metaphorical cognition arises from the mapping of two conceptual domains onto each other. According to the “Anthropocentrism”, people tend to know the world first by learning about their bodies including Apparatuses. Based on that, people begin to know the material world, and the human body part metaphorization emerges as the times requires. Because mankind possesses same body structure, perceptive organs, same perceptive and cognitive abilities, so people have many similarities in their cognition. At the same time, both the metaphor thinking and the conceptive system are from human living experiences. As a kind of thinking mode and behavior mode, the metaphor doesn’t exist along, and it can not break away from the social and cultural environment, and it must be combined closely with certain language situation and culture. As a result of cultural influence, metaphor shows its unique nationality. Taking English-Chinese animal and human body part words as the example, the cultural cognitive difference of Chinese and English metaphors is analyzed in the article.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".