The impacts of live streaming and Twitch.tv on the video game industry
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
This article explores the growing importance of live streaming, specifically on website and platform Twitch.tv, to the games industry. We focus not on live streaming as a form of media production and consumption, but instead explore its newly central role in the contemporary political economy of the whole video games ecosystem. We explore three cases: streaming newly released games and the attendant role of streaming in informing consumer choice; the visibility and added lifespan that streaming is affording to independent and niche games and older games; and the live streaming of the creation of games, shedding light on the games industry and subverting ordinarily expensive or highly competitive game-design courses, training and employment paths. To do so, we draw on empirical data from offline and online fieldwork, including 100 qualitative interviews with professional live-streamers, offline ethnography at live-streaming events, and online ethnography and observation of Twitch streams. The article concludes that live streaming is a major new force in the games industry, creating new links between developers and influencers and shifting our expectations of game play and game design, and is consequently a platform whose major structural effects are only now beginning to be understood.
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 | 0.001 | 0.001 |
| 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