Verified play, precarious work: GamerGate and platformed authenticity in the cultural industries
Bibliographic record
Abstract
This article argues that GamerGate, a critical hashtag event in the history of digital harassment, is key to understanding contemporary identity verification systems and digital labour. We build our argument from a comparative analysis of two case studies: (1) digital journalistic responses to GamerGate and (2) Twitter’s account verification ‘checkmark’ system from 2021 to 2022. These phenomena showcase the linkages between the gendered and raced policing of journalists and users during GamerGate and the rise of ‘authenticity’ as a key resource for journalists and other platformed creators in the present. We draw on digital games, journalism and critical media studies to analyse the work of ‘authenticity’. We argue that platform affordances such as identity verification badges are fundamentally implicated in the work of users to appear ‘real’, even as the visibility requisite for realness brings uneven risks for marginalised cultural workers.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".