Act Like a Woman, Play Like a Man: Manhood Acts and the Gendered and Racialized Organization of Online Professional Streamers
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it