Who Speaks Tho Fan? Deconstructing the Constructed Language of <i>Jade Empire</i>
Bibliographic record
Abstract
Abstract Despite being the largest East Asian country, China is underrepresented in video games, particularly those produced in the West. This article examines the video game Jade Empire (2005), developed by the acclaimed Canadian studio Bioware. Despite its age and now historical status in video game culture, Jade Empire remains one of the few relatively successful standalone video games based on Chinese culture (or appropriation thereof) created in the West. In addition to the borrowed elements of Chinese culture, the game uniquely introduces its own constructed language, Tho Fan, for world‐building. This article contextualizes this constructed language within the broader scope of constructed languages in Western fantasy and video games in particular, analyzing its creative and communicative potential. My analysis suggests that Tho Fan acts more as a tool of exclusion than of immersion, aligning with postcolonial critiques of video games. Conversely, Tho Fan has subversive potential when viewed as a metaphor for the inability of “the Subaltern” to “speak.” This ultimately presents a paradoxical issue of perceived authenticity in fantasy worlds that inherently defy historical accuracy.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".