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Record W4220926149 · doi:10.1177/15274764221080930

“Never Battle Alone”: Egirls and the Gender(ed) War on Video Game Live Streaming as “Real” Work

2022· article· en· W4220926149 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.

Bibliographic record

VenueTelevision & New Media · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVideo gameExpansiveBattleSociologyAmbivalenceEntertainmentWork (physics)Media studiesGame studiesMultimediaPolitical scienceComputer sciencePsychologySocial psychologyHistoryLawEngineering

Abstract

fetched live from OpenAlex

From 2018 to 2021, the “egirl” witnessed a radical shift from her origins as a sexualized slur in online gaming. Through critical discourse analysis of news media of this period, this paper interprets this transformation within two primary phenomena: (1) the growth of women game influencers who reclaimed “egirl” slurs in their self-branding and (2) the launch of “Egirl.gg,” a platform for paid gaming companions. I argue that live streaming platform Twitch.tv, and the expansive ecosystems of labor its demand from streamers, were integral to this re-authorization of who can play as themselves in a patriarchal gaming culture. Here, I extend Ergin Bulut’s framework of “ludic authorship” to delineate how stakeholders in game streaming industries masculinize the cultural labor of “authenticity.” The ambivalent embrace of “egirling” via streaming cultural logics further complicates the work of women gamers who must work harder to realize careers in platformed entertainment.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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