Bibliographic record
Abstract
Abstract Asian Americans have frequently been associated with video games. As designers they are considered overrepresented, and specific groups appear to dominate depictions of the game designer, from South Asian and Chinese immigrants working for Microsoft and Silicon Valley to auteur designers from Japan, Taiwan, and Iran, who often find themselves with celebrity status in both America and Asia. As players, Asian Americans have been depicted as e-sports fanatics whose association with video game expertise—particularly in games like Starcraft, League of Legends, and Counter-Strike—is similar to sport-driven associations of racial minorities: African Americans and basketball or Latin Americans and soccer. This immediate association of Asian Americans with gaming cultures breeds a particular form of techno-orientalism, defined by Greta A. Niu, David S. Roh, and Betsy Huang as “the phenomenon of imagining Asia and Asians in hypo- or hypertechnological terms in cultural productions and political discourse.” In sociology, Asian American Studies scholars have considered how these gaming cultures respond to a lack of acceptance in “real sports” and how Asian American youth have fostered alternative communities in PC rooms, arcades, and online forums. For still others, this association also acts as a gateway for non-Asians to enter a “digital Asia,” a space whose aesthetics and forms are firmly intertwined with Japanese gaming industries, thus allowing non-Asian subjects to inhabit “Asianness” as a form of virtual identity tourism. From a game studies point of view, video games as transnational products using game-centered (ludic) forms of expression push scholars to think beyond the limits of Asian American Studies and subjectivity. Unlike films and novels, games do not rely upon representations of minority figures for players to identify with, but instead offer avatars to play with through styles of parody, burlesque, and drag. Games do not communicate through plot and narrative so much as through procedures, rules, and boundaries so that the “open world” of the game expresses political and social attitudes. Games are also not nationalized in the same way as films and literature, making “Asian American” themes nearly indecipherable. Games like Tetris carry no obvious national origins (Russian), while games like Call of Duty and Counter-Strike do not explicitly reveal or rely upon the ethnic identities of their Asian North American designers. Games challenge Asian American Studies as transnational products whose authors do not identify explicitly as Asian American, and as a form of artistic expression that cannot be analyzed with the same reliance on stereotypes, tropes, and narrative. It is difficult to think of “Asian American” in the traditional sense with digital games. Games provide ways of understanding the Asian American experience that challenge traditional meanings of being Asian American, while also offering alternative forms of community through transethnic (not simply Asian) and transnational (not simply American) modes of belonging.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".