The Affective Labor and Performance of Live Streaming on Twitch.tv
Bibliographic record
Abstract
This article explores affective and immaterial labor on the leading live-streaming platform, Twitch.tv, which boasts over one hundred million regular viewers and two million regular broadcasters. This labor involves digitally mediated outward countenance, including being friendly to viewers, soliciting donations, building parasocial intimacy with spectators, and engaging audiences through humor. We offer an examination of streamers broadcasting as a “character,” which we situate within the context of play becoming work, the labor of performance and acting, and the economic compulsions that shape cultural labor on Twitch. We draw on hundred interviews with professional and aspiring-professional game broadcasters conducted in 2016 and 2017 at gaming events across the United Kingdom, the United States, Germany, and Poland, alongside ethnographic research. This inquiry into the dynamics of digital games and labor underscores the importance of studying live streaming as part of a wider critical investigation of contemporary digital work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".