Streaming ambivalence: Livestreaming and indie game development
Bibliographic record
Abstract
owe their ongoing success in no small part to their massive uptake by streamers, and triple-A releases from major publishers can reliably expect significant attention on streaming platforms. But what about smaller, lower budget games? For independent game developers, the costs and benefits of streaming are less clear. Based on interviews with small commercial indie developers in Toronto and Montréal, this article critically examines different discourses around streaming and commercial indie games, focusing on developer perceptions of the benefits and risks of streaming and its impacts on indie game-making practices, including production, promotion, and community-building. Contrary to persistent popular myths about streaming as the key to 'discoverability', commercial indie game development remains a precarious form of cultural work, and indie games collectively attract only a tiny fraction of the overall audience on streaming platforms. There is a high level of uncertainty about the factors that led to a given game's success, leaving many indie developers ambivalent about leveraging influencer attention and even as they commit significant time and energy trying to doing so.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".