A Practical Study on the Translation of Science Fiction with Generative Artificial Intelligence— A Case Study of The Three-Body Problem II: The Dark Forest
Bibliographic record
Abstract
To explore the capability boundaries and strategy preferences of generative artificial intelligence in the translation of science fiction literature, this paper takes Liu Cixin's "The Three-Body Problem II: The Dark Forest" as the corpus and adopts a case study approach to conduct a systematic analysis of the translation outputs of a specific artificial intelligence model. The research finds that artificial intelligence can flexibly apply foreignization and domestication strategies: when dealing with names of people and places and core science fiction concepts, it tends to use transliteration and literal translation and other foreignization strategy to preserve the uniqueness of the text; when handling idioms, cultural images and complex sentence structures, it tends to use addition, omission, modification and free translation and other domestication strategies to ensure the readability of the translation. However, there are still clear comprehension gaps when dealing with puns, character names, and other texts that contain deep cultural and pragmatic information. The research concludes that current generative artificial intelligence shows significant auxiliary potential for science fiction translation, but it cannot completely replace human translators in cultural decoding and creative rewriting. Human-machine collaboration remains the ideal model for literary translation.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".