An eco-translatology-based comparison of localization in simplified and traditional Chinese locales
Bibliographic record
Abstract
Abstract Through an examination of the three dimensions of eco-translatology, this study aims to identify the localization characteristics and differences in Hearthstone between the simplified and traditional Chinese locales, with the intention that the results can serve as guidelines for future localization projects for the two markets. In the linguistic dimension, faithful translation is the most frequently adopted strategy, although traditional Chinese teams sometimes rewrite text to enhance the entertainment level. In the cultural dimension, straightforward and simple wording in the source text again results in the most frequent use of faithful translation, and the traditional Chinese team still employs rewriting to create humor. Finally, to fulfill the expected effect in the communicative dimension, five transcreation strategies are identified in both localized versions, namely, re-creation of wordplay, contextualization by rewriting, referring to the Warcraft universe elements or regional expressions, imitation of the Chinese literary form, and use of political satire. The current findings can inform future localization teams, as well as future researchers who aim to investigate the strategies used in the two Chinese locales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".