Bibliographic record
Abstract
This paper studies the system of conceptual metaphors for thinking in Modern Mandarin Chinese. It looks into the frequency, types of metaphors and the ways they are realized in Language. The present research concentrates on five commonly used words for thinking, namely 想 xiang, 认 ren, 觉 jue, 觉得 juede, and 认为 renwei. The expressions about thinking used in the research are taken from spoken and non-spoken Modern Mandarin Chinese corpora. All examples were reviewed and metaphorical examples were identified and classified according to the metaphor types as distinguished by Lakoff and Johnson 1999. Series of research done in the sphere of cognitive science proved that some expressions about thinking are generally structured by conceptual metaphor based on the source domain of our embodied experience. However it was unclear how often metaphoric expressions are used in language compared to the non-metaphoric ones. The paper also looks into the difference in metaphor use in spoken and non-spoken Mandarin Chinese, metaphors of heart and head as the locus of thinking in Chinese. The research has shown approximately every fifth common expression about thinking is metaphorical, while container and path metaphor are most widely used to talk about thinking. Moreover, a large number of metaphors in expressions about thinking are realized through grammatical patterns, such as resultative constructions, and are generally not perceived as metaphorical. The results suggest that possibly different types of metaphor dominate in thinking expressions in Chinese and other languages. The research also indicates that in learning and teaching Chinese as a foreign language, conceptual metaphor awareness is necessary for grammar literacy and language proficiency, since a large number of fixed metaphoric constructions are realized in grammar. Generally the paper suggests that while most metaphors for thinking are universal, there are often differences in the frequency and the ways to use the metaphors. Thus such cultural variations can often result in different conceptualizations of an abstract concept or higher sensitivity to one type of metaphor but not the other.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".