Translation as Representation: Western Imagination of China from the Eighteenth Century to the Present
Bibliographic record
Abstract
This article explores how China has been imagined in Western literature and culture from the eighteenth century to the contemporary era, using a series of emblematic figures to examine practices of appropriation and troping. It seeks to reveal how the historical construction and representation of China is refashioned in our time, against the background of China’s rise to global power and a concomitant growing sense of doubt, fear and hostility in Europe and North America surrounding its ascendency. Two case studies are employed to elaborate on what this article considers to be exemplifications of Orientalism in Sino-Western cultural history: Daniel Defoe and Voltaire in Enlightenment Europe, and Western popular culture in the twentieth and twenty-first centuries. In doing so this article addresses how China has incrementally come to be translated and represented as a principal Other for Euro-America, and how their perceptions of, and writings about, China today echo and reinforce its translations and representations produced by the Defoe-Voltaire axis: the binary schema of demonization and romanticization. It concludes that this mechanics at work in framing China for reception and consumption by the Anglophone readership provide effective means of translating and representing China as an ideologically menacing Other, thereby begetting self-perpetuating representations.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".