Room with a viewership: visibility work & Twitch.tv in the domestic context
Bibliographic record
Abstract
This paper argues for the domestic worksites of marginalized game streamers as a crucial site for understanding the politics of visibility work on platforms. Twitch.tv stands out as Amazon’s world-leading platform for live video entertainment. Through ethnographic interactions with Twitch creators (n = 12), I clarify the challenges marginalized individuals face in livestreaming’s platformization of game cultures. While streaming provides opportunities for creative self-expression and shields against hostile gaming communities, it also relocates streamers’ precarities before audiences to the sensitive enclaves of domestic space. Consequently, Twitch streamers must delicately balance self-presentation and discretion due to the looming threat of over-exposure in a historically unfriendly gaming culture. By exploring these dual experiences from the standpoint of vulnerable creators, this paper offers insights into the intricate strategies of visibility management in social media work. It also contributes to the ongoing discourse on the construction of online presence, extending into feminist theories of domestic work and the social reproduction of online game environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".