Dancing with Ideology: Grammatical Metaphor and Identity Presentation in Translation
Bibliographic record
Abstract
This paper begins with an account of a high-profile political speech event centring on a Chinese slangy expression ‘[we]bu zheteng’ when it was used by the then Chinese President Hu Jintao in a 2008 speech, of which the Chinese government preferred a zero-translation despite the existing translations and various choices already available in Chinese-English dictionaries. The paper then discusses from the perspective of grammatical metaphor how and why an innocent-looking pragmatic usage has given rise to a series of ideologically charged debates over its translation. To that end, the paper conducts a critical review of grammatical metaphor, a key Systemic Functional Linguistic concept in describing congruence-to-metaphor evolution of language. Our cross lingual observation of this translation-related speech event enables us to argue that different textual means of presentation/concealment of human participation in transitivity are key to accounting for the discursive function of grammatical metaphors and to discerning the “chain” between congruent and metaphorical expressions. In the light of concealment of human participation in the transitivity process, the paper also observes that it is the association between the vague self-referencing ‘we’ and the adverse actions/situations, that is, (causing) commotions, alluded to by the termzhetengthat has made a semantically explicit translation ideologically less desirable. As such, this operation of zero translation appears to be an instance of discursive manoeuvre rather than a sign of semantic impasse. To substantiate its theoretical claim, the paper relates the case to some similar political speech events in the world’s political arena and demonstrates how, prompted by this functional awareness of grammatical metaphors, one may devise a translation with better informed sensitivity to identity presentation/concealment in discourse.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".