“Never Battle Alone”: Egirls and the Gender(ed) War on Video Game Live Streaming as “Real” Work
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".