AUTHENTIFICATION AT THE EDGE: #”GAMERGATE”-ING THE ASCENT OF THE VERIFIED INTERNET
Bibliographic record
Abstract
While platforms may be hard to know, they are increasingly invested in knowing—and policing— users. From “verified accounts” to “two-factor authentication,” platform affordances have proffered metrics of “authenticity” as an antidote to the uncertainties of attention economies, which are ostensibly saturated by fake news, misinformation, and algorithmic radicalization (Haimson & Hoffman, 2016; Caplan, 2020). Yet the platformization of realness claims are often weaponized against the most marginalized, as evidenced by recent events like PornHub’s demonetisation of unverified accounts. Early hashtag harassment events like #GamerGate foreshadowed the gendered consequences of digital realness regimes. In the ascent of “verified account” castes and other digital authenticators, the traditional “black box” conceptualization of platforms rings increasingly untrue. Rather, we argue, the algorithmic reality for marginalized users better resembles Wile E. Coyote’s painted tunnels on the side of mountains: vortexes of selective porosity that invite some roadrunners and flatten others. Through a Critical Discourse Analysis of #GamerGate coverage from 2014 and 2015, we attend to how ideologies of “realness” reproduce along gendered and racialized lines. Our paper builds on recent work on how the ideation of “realness” embeds forms of communicative and audience-managing labour among networked creators (Abidin 2016; Banet-Weiser, 2012; Duffy, 2017). The ascent of “authentification-as-safety is historicized within the hashtag harassment event “GamerGate,” our case study and pivotal moment in the platform veracity ecosystem when influencers and journalists were exhorted to authenticate their lives or lose their livelihoods. Everyone on the internet knows you’re a dog. Now what?
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".