THE SOCIO-TECHNICAL ENTANGLEMENTS OF LIVE STREAMING ON TWITCH.TV
Bibliographic record
Abstract
The website and platform Twitch.tv is the overwhelming market leader in the live broadcast (“streaming”) of user-created videos over the internet, known primarily for the streaming of video game play. In both 2016 and 2017 over two million people regularly broadcast on the platform, resulting in over a million years of video content in total viewed by over one hundred million people (Twitch, 2017). The deep newness of this phenomenon, alongside the many elements that constitute it, make it an important site for studying digital labour, co-production, and gaming culture. In this paper we focus on three elements of the conference theme: the shifting political and creative economies of streaming media, in our case Twitch; social media, platforms, podcasts, and actors in online networks; and the materialities of data, in our case a million years of video content. Specifically, we consider the entangling of the technical and social dimensions of the Twitch phenomenon: how these elements shape the labour of Twitch streamers, audience engagement with the platform, and Twitch’s wider position in contemporary media production. To do so we draw upon semi-structured interviews with over one hundred professional streamers on the Twitch platform, lasting between ten minutes and one hour, alongside at least one hour of ethnographic observation from over two hundred Twitch channels and ethnographic work from almost a dozen gaming events in the United Kingdom, United States, Germany and Poland in the past two years.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 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".