Bibliographic record
Abstract
This paper analyzes the metaphorical structure of the domain of death in Chinese within the framework of the conceptual metaphor theory. The Chinese data come from an essay collection and two dictionaries, one general dictionary, the other a dictionary of euphemisms. It aims to account for the way the Chinese conceptualize death metaphorically in terms of a limited system of metaphors, metonymies and image schemas which are grounded in our bodily and social experience, with the goal of identifying cross-linguistic/cross-cultural variation in the types of metaphorical mappings proposed by Lakoff and Turner for English. The analysis fails to reveal a single coherent conceptual organization underlying Chinese death expressions. The data suggest a high degree of similarity between English and Chinese in the types of metaphorical mappings and support the claim that primary metaphors are shared by all human languages. However, cross-linguistic discrepancy is observed in complex mappings. One potential reason for this is that the cultural models of death and afterlife are very much blended with the religious formulations of these concepts, and given the vast differences between the religious formulations of death and afterlife in Chinese and Western religions, it is a likely outcome that the metaphors based on these cultural models will be different. In addition, the Chinese emphasis on the social roles and responsibilities of the individual and the belief that life and death form a continuum rather than a break give rise to various mappings and death expressions that have no counterpart in English.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 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".