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Record W2946997610 · doi:10.1177/1527476419851078

Speaking in Public: What Women Say about Working in the Video Game Industry

2019· article· en· W2946997610 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTelevision & New Media · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsYork UniversityOntario Tech University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVideo gameFace (sociological concept)Rhetorical questionAffect (linguistics)Power (physics)SociologyPublic relationsField (mathematics)Media studiesGender studiesPolitical scienceMultimediaSocial scienceComputer scienceLinguistics

Abstract

fetched live from OpenAlex

Since the 1990s, conversations about the dearth of women working in the video game industry have centered on three topics: (1) ways to draw more women into the field, (2) the experiences of women working in the industry, and (3) the experiences of those who once worked in the industry but left. Although there has been considerable research on the conditions and occupational identities of video game developers, less scholarly attention has been devoted to women in gameswork, the barriers/obstacles and challenges/opportunities they face, and how they talk about their experiences. This article offers a feminist approach that demonstrates how discourse focused on affect can be reread as intimately related to silences about power and how the rhetorical constraints that public speech imposes upon what can be said about “women in games” aid us in understanding what might remain unspoken, and why.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.299
Teacher spread0.254 · 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