When Deciding to Translate Means Risking Your Reputation: How an American Translator Became a “Spy,” and a Chinese Author, an “Enemy from America”
Bibliographic record
Abstract
While research on the role of translation in society largely focuses on the reception of translated texts, this article calls for a closer look at the decision to translate. It proposes that, on a micro-level, the decision to translate, in the context of an ideological and political conflict, has the potential to subvert the image of authors and translators as perceived by certain groups of people. It reveals how opinions regarding translators and authors are often a product of ideological stances rather than widespread reading of either the authored text or its translation. In this case, it is not a collective reading of the translation itself that sways the perception but, rather, a political “reading” of the translator’s and author’s respective images, which consequently influences their reputation within these groups. This article investigates the translation of a “diary” that recorded events during the Wuhan lockdown (January-April 2020) and garnered much attention on Weibo, China’s largest social media platform. Comments shared on Weibo about the author, Wang Fang, also known as Fang Fang, and the American translator, Michael Berry, were significantly different before and after the publication of Berry’s translation, intitled WuhanDiary. By examining a sample of Weibo users’ reactions, the article seeks to understand the rationale behind the changing perceptions of the author’s and the translator’s image. It argues that Berry, through his decision to translate, comes to be perceived by Weibo users as a “spy,” while Fang Fang, having given her consent for her “diary” to be translated, is then perceived as an “enemy from America.” Translation is thus seen to play a significant role in subverting both an author’s and a translator’s reputation at the micro-level.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".