Fan translation of video games in China: The case of <i>Deeptrans</i>
Bibliographic record
Abstract
Following the Chinese State Council’s ban on commercial gaming consoles and the rise of computer game piracy, foreign video game companies began to withdraw from investing in Chinese game translations in the early 2000s. In response, fan translation emerged as an alternative for Chinese gamers. ‘Fan translation’ refers to unofficial translations produced by enthusiasts for various types of media. Specifically, fan translation of video games involves online collaboration among players to translate foreign titles, with the goal of providing an accessible and enjoyable experience that stays true to the original content. In light of the growing community of video game fan translators in China, this study offers a comprehensive examination of the phenomenon. The research focuses on the internal dynamics and organization of Deeptrans, a Chinese group dedicated to video game fan translation. The article begins by exploring the challenges and defining characteristics of video game fan translation, supported by a brief literature review due to the limited scholarship available on the subject. Next, the methodology is outlined, detailing a qualitative case study approach that analyzes archival documents produced by Deeptrans members during their translation of the video game X4: Foundations (Egosoft, 2018). The third section presents and analyzes the results, highlighting the methods employed by Deeptrans members to carry out their translation tasks, which align with the concept of a community of practice. This community shares a common interest, set of challenges, and passion for the video game X4: Foundations, fostering a knowledge base that encompasses orientation, linguistic skills, and expertise developed through regular interaction. Members utilize computer tools to collaborate effectively, operating within an informal hierarchy characterized by role division and distributed leadership. Collaborative discussions play a pivotal role, enabling members to support one another and find optimal solutions to the challenges encountered in their work. Through collective participation and regular interactions, individual and practical knowledge becomes objectified and transformed into a shared repertoire. The primary aims of this community of practice are to strengthen group cohesion, improve the efficiency of game translation tasks, and facilitate collective learning. The concluding section of the article revisits the study, providing a deeper understanding of the phenomenon as a whole, including considerations of copyright and translation quality.
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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 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".