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Record W2614977621 · doi:10.1177/1555412017708936

Professional Norms and Race in the North American Video Game Industry

2017· article· en· W2614977621 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGames and Culture · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeVideo gameConstruct (python library)RacismSociologyRace (biology)Game DeveloperHedgePublic relationsSocial psychologyGame designPsychologyComputer scienceGender studiesPolitical scienceMultimediaLinguistics

Abstract

fetched live from OpenAlex

This article examines North American (i.e., Canada and the United States) video game developers’ understanding of race, how they construct narratives when they include characters of different races, and some of the pressures that may shape that process. Discourse analyses of semistructured interview texts found that video game developers operate under an internalized pressure to create game narratives that are quickly understandable and, thus, sellable. This pressure is normatively internalized in the profession as an attempt to hedge against market uncertainty. Video game developers, therefore, depend on social beliefs from the “real world” to inform how video game players might receive their games as well as narratives and themes from past texts such as the works of J. R. R. Tolkien. Therefore, this article argues that racism might be enabled because it is believed to be a hedge against market uncertainty.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.309
Teacher spread0.294 · 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