The Gendered Work of Play: Twitch Livestreaming and the Home
Bibliographic record
Abstract
This article-based dissertation chronicles the work conditions of gendered and/or racialized game streamers on Amazon’s livestreaming platform, Twitch.tv. Drawing primarily on semi-structured interviews and participant observation conducted with game streamers in Canada and the United States in 2022, I show the significance of domestic spaces for in the production of live gaming broadcasts for players historically vulnerable within white- and male-dominated gaming technocultures. I also conduct a critical discourse analysis of game streaming industry media coverage to contextualize how players of historically marginalized backgrounds navigate the challenges of building audiences on gaming-focused platforms such as Twitch.Over three papers, I detail how domiciles and familial networks can reproduce systemic barriers for racialized and gendered Twitch streamers, even as home studios offer vital resources for navigating the risks of professional gaming in the creator economy. First, through critical discourse case studies of news media on gendered streamers, I detail how gaming industry and constructions of authenticity impose a culture of scrutiny and suspicion against women game streamers. Second, I demonstrate that the online performances created by livestreaming depend on offline non-player support, as streamers often enlist non-streamer cohabitants as collaborators in the production of broadcasts. Finally, I document the physical staging of home production sites by gendered and/or racialized game streamers. I detail how everyday household surfaces and practices allow streamers to calibrate between visibility and invisibility before online audiences, which is a crucial defensive tactic in the face of hostile technocultures. This dissertation argues that the status of game livestreaming at home remains ambivalent for racialized and gendered participants. It traces how the production of livestreaming is contingent on household resources and relationships that are not immediately visible to platform metrics. I end by looking back at these explorations and consider to what degree the quotidian practices of the home and its cohabitants might support future critical feminist studies of games, platforms, and cultural production.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".