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Asian Americans and Digital Games

2018· reference-entry· en· W2906646416 on OpenAlexaff
Christopher B. Patterson

Bibliographic record

VenueOxford Research Encyclopedia of Literature · 2018
Typereference-entry
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBasketballAsian gamesVideo gameMedia studiesOrientalismGender studiesPoliticsHistorySociologyAdvertisingPolitical scienceMultimediaLaw

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.001

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.

Opus teacher head0.040
GPT teacher head0.373
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations22
Published2018
Admission routes1
Has abstractyes

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