Bibliographic record
Abstract
This paper is derived from a larger project that examines the experiences of women who live-stream video games on the Twitch.tv platform. To date, much of the research that has been done in the area of streaming is concerned with streamers who have a large following and/or derive their main source of income from streaming. Rather than directing more attention to those streamers who have attained ‘success’ as Twitch would frame it, this study is centered around a group of streamers unique from those who are typically the focus. First, I discuss the ways monetization influences community building. Second, I discuss some of the implications of paying for attention. Third, I discuss the pressure streamers feel to perform a particular kind of authenticity around monetization. Finally, I will discuss how monetization creates friction and competition between streamers. This work contributes the perspectives of 5 women whose experiences have been largely overlooked by existing research about streaming, as well as the analysis of another 50 Twitch channels run by women from diverse backgrounds and streaming interests. These findings demonstrate that the monetization features available to streamers and the everyday practices that have emerged through Twitch centered around monetization have a lot of influence over how people relate to each other, even for those streamers who are not trying to monetize their channels.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".