Act Like a Woman, Play Like a Man: Manhood Acts and the Gendered and Racialized Organization of Online Professional Streamers
Bibliographic record
Abstract
Professional gaming is a billion-dollar industry with some earning incomes comparable to those of popular athletes and entertainers. The rapid rise of professional gaming owes its success to the advent of Twitch.tv, a video streaming platform that enables streamers to broadcast a live feed of their gameplay while interacting with fans. While white men have mostly dominated this arena, white women and people of color are beginning to rise in the ranks of professional streaming. In this article, we examine how online platforms like Twitch represent a new type of workplace that is organized around geek masculinity and manhood acts, establishing and perpetuating hierarchies of masculine dominance and white privilege. Analyzing interaction patterns of streamers and their viewers via publicly available text and video data, we find that men streamers and their audiences create a hostile work environment for white women and people of color online in three ways. First, gendered communication patterns of streamers uphold the gender hierarchy. Second, communication patterns of the audience rely on racialized manhood acts that put women in their “place” and perpetuate white supremacy through racialized stereotypes. Finally, manhood acts based on sexual harassment towards women, including racial epithets, signal male dominance and the dominance of white culture. These virtual manhood acts perpetuate an organizational structure of sexism and racism that establishes a hierarchical workplace, placing white men “geeks” at the top and reinforcing gender and racial inequalities in the workplace.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".