RECONTEXTUALIZING VIOLENCE IN REAL TIME: LIVE STREAMING & THE GOVERNANCE OF INCONSISTENCY ON TWITCH.TV
Bibliographic record
Abstract
How does inconsistency become an institution? Here, we examine platform governance and moderation on Amazon’s Twitch.tv as a cultural practice. Through case studies and thematic analysis, we showcase moments of regulatory inconsistency that are constitutive of how Twitch manages harm. Our analysis identifies contexts, temporalities, and violence as critical themes for identifying Twitch’s inconsistent moderation. We offer a playbook for better understanding live streaming platform governance as an iterative process which frequently targets vulnerable streamers. We applied thematic analysis to two case studies to document how regulatory inconsistencies are directed at historically marginalized streamers. These cases include Twitch’s response to: 1) Kai Cenat’s impromptu community meet-up which was labeled a riot by the NYPD and 2) When Twitch modified their clothing and attire policy three times in one month to curtail the so-called ‘topless meta’, where a handful of women staged their cleavage to imply full nudity and optimize viewer engagement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".