“You’ve Got to Put in the Time”: Neoliberal-Ableism and Disabled Streamers on Twitch
Bibliographic record
Abstract
This concept paper builds upon nascent research analyzing disability and the practice of videogame livestreaming on Twitch.tv. While a growing amount of scholarship analyzes the structure and organization of Twitch as a platform more broadly, with some attending to the platform’s marginalization of women and BIPOC streamers, few studies investigate the challenges that Twitch’s features and structures present to disabled streamers. This paper addresses this gap in the literature, considering the ways in which Twitch offers disabled streamers unique economic and community-building opportunities through its monetization and identity tag features while simultaneously presenting barriers to disabled streamers through these very same features. Utilizing a critical disability studies perspective and drawing upon forum posts made by disabled streamers and interviews with disabled streamers from online gaming news websites, I argue that Twitch reifies forms of neoliberal-ableism through its prioritizing of individual labour, precarious forms of monetization that necessitate cultures of overwork and ‘grinding’, and targeted harassment, known as hate raids, against disabled and other marginalized streamers to ultimately create a kind of integrative access where disability is tolerated but not valued.
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.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".