The imperative to be seen: The moral economy of celebrity video game streaming on Twitch.tv
Bibliographic record
Abstract
In this paper we examine the pursuit of celebrity through the live broadcast (‘streaming’) of video games as an expression of an emerging moral economy of contemporary digital capitalism. Live streaming is a novel form overwhelmingly found amongst young people disproportionately harmed by the economic crisis, and we propose that the contraction of employment opportunities is giving rise to a strong imperative to be seen, which finds an outlet in the practices of self-presentation, self-promotion and entrepreneurial enterprise that are central to financially-successful live streaming. We first outline relevant contemporary economic conditions, the disproportionately high prizes at the top of career paths, the attendant lures of fame and fortune, and how the politics of play have been affected by these changes. We then explore Twitch.tv (the leading game live streaming platform) as our case study, covering how streamers make themselves appealing, market themselves, profit, and how the platform’s affordances are interwoven into these questions. In doing so, we present Twitch as illustrative of the broader phenomenon of ‘digital celebrity’ and argue its practices reflect changes in work opportunities and social identity. In particular, we show that Twitch is a platform that allows neoliberal aspirations to play out through competitive performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".