Two Enlightenments on Chinese Literature Going Out: By Comparing Wolf Totem With China in Ten Words
Bibliographic record
Abstract
China still has a serious cultural deficit in international cultural exchanges, and few contemporary Chinese literature works are successfully translated and communicated in foreign countries. However, both Wolf Totem and China in Ten Words have been on Amazon’s list of best sellers after their English versions were published in the United Kingdom and the United States. Based on comparison between them, this paper aims to find the similarities and differences between Wolf Totem and China in Ten Words , and then get two enlightenments on Chinese literature going out: First, successful translation and communication of Chinese literature works share some similarities. Successfully finding the similarities will be conductive to Chinese literature going out. Second, not all successful translation and communication of Chinese literature can objectively and truly present China to the world, taking China in Ten Words as an example. Therefore, the Chinese government should play a more active role in sending out Chinese literature works which could construct China’s national image.
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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 | 0.006 | 0.009 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".