Translation Research on Conceptual Metaphor in the 2023 Chinese Government Work Report
Bibliographic record
Abstract
In traditional rhetoric, metaphor is simply a rhetorical device used to make the mentioned things more understandable. It was not until 1980 that two cognitive linguists, George Lakoff and Mark Johnson (1980) argued in Metaphors We Live By that the essence of metaphor is the cognitive mechanism by which abstract things are explained through concrete things, shifting the study of metaphor from the linguistic level to the cognitive level. Later on, Lakoff (1996) analyzed political metaphor from a cognitive perspective for the first time in Moral Politics, which drives the upsurge of research on conceptual metaphor in political discourse. Political discourse usually uses metaphor to conceptualize the political ideas and issues it aims to disseminate, and the use of conceptual metaphor is closely related to national culture, so the translation of conceptual metaphor has become the key to the overseas publicity of political discourse. On March 5, 2023, Premier Li Keqiang delivered Chinese Government Work Report at the opening meeting of the first session of the 14th National People’s Congress. After reading the official translation on www.china.org.cn, the authors find that the Report contains a wealth of conceptual metaphors, and whether the translation of these metaphors is appropriate or not will affect the accuracy of people’s understanding of the Report. Based on Lakoff and Johnson’s (1980) conceptual metaphor theory and Group’s (2007) metaphor identification procedure, this paper takes the 2023 Chinese Government Work Report and its English translation version on www.china.org.cn as the research corpus. Through manual screening, classification and statistics of conceptual metaphors, this paper explores ten types of conceptual metaphor models, namely human metaphor, journey metaphor, war metaphor, cultural metaphor, architecture metaphor, water metaphor, animal and plant metaphor, machine metaphor, line metaphor as well as object metaphor. Based on Xiao’s (2005) cognitive strategy of metaphor translation, this paper also analyzes the translation of ten types of conceptual metaphors. This paper attempts to explore the following three research questions: (1) What are the types of metaphorical patterns in the Report? (2) How are the conceptual metaphors used in the Report and what cultural connotations and images are conveyed by them? (3) How to effectively translate conceptual metaphors in the Report to achieve a better understanding of the target audience? Microsoft Office (Word and Excel) is used as a statistical tool and a mapping tool to count specific conceptual metaphor categories and record typical metaphor keywords, and visualize the data of the proportion of various types. This paper tries to summarize and analyze the cultural connotations and images conveyed by the conceptual metaphors, so as to provide help for the English translation of Chinese political discourse and promote the international dissemination of Chinese political ideas. Through the analysis of conceptual metaphors, we can judge that although the political concepts in the Report is abstract, conceptual metaphors can express them more concretely and more easily understood by the audience through the mapping from the source domain to the target domain.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".