A Comparative Study of English and Chinese Animal "Rooster" Metaphor From the Cognitive Perspective
Bibliographic record
Abstract
It is universally accepted that cognitive linguistics is a relatively new school of linguistics, and one of the most innovative and intriguing approaches to the study of language and thought. During the past two decades, this cognitive science entered into a new era, especially after Lakoff & Johnson came up with the conceptual metaphor. It argues that our understanding of the world is experiential rather than literal or direct corresponding to and external reality. Besides, our reasoning involves metaphorical inferences; our categories of entities are mostly metaphorical and imaginative. Metaphor is ubiquitous in our thought, action, human language as well as a significant cognitive instrument by which human beings perceive, categorize and conceptualize the world. Among them, animal metaphor is an important category for their rich images and intimate relationship with human beings. Thus the attributes of animals are inevitably mapped onto those human beings. Many studies have been made about animal metaphor either from cognitive angle or cultural perspective. But animal metaphor is only taken as a whole subject to carry out different studies. Yet this paper will merely discuss metaphors on “rooster” in English and Chinese from cognitive perspective, which aims to contrast and discover the cognitive similarity and differentiation about rooster through a detailed analysis of metaphorical expressions in both languages, and at the same time this paper hopes to make a certain contribution in realizing high-quality cross-cultural communication.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".