Lessons from #GamerGate: Complicating virtual harm and reassessing frameworks for virtual harm assessment
Bibliographic record
Abstract
Beginning in summer 2014, a series of sustained misogynistic attacks against women in the video game industry coalesced online under #GamerGate. In this study I conduct a virtual ethnography of various online fieldsites hosting #GamerGate discussions, with the goal of complicating prevailing understandings of virtual harm. I draw from Feinberg (1987) to operationalize harm as that which damages an interest. I suggest that discourses in public policy, popular media and popular culture can oversimplify representations of virtual harm, theorizing an ontology of virtual harm that acknowledges a more nuanced range of factors that can impact how harm manifests within virtual contexts.I add complexity to prevailing narratives of #GamerGate by highlighting that users throughout my fieldsites consistently perceive a range of virtual behaviours, including criminal direct harassment (Lenhart et al., 2016) and the nonconsensual disclosure of private personal information, to be harmful. I submit that users' (infrequent) engagement in these "universal harms" is not, as prevailing representations of #GamerGate can suggest, reflective of community or cybercultural affiliation.I move forward to examine how users participating in #GamerGate discourses can disagree in their conceptualizations of virtual harm. Based on these points of contention, I draw from O'Sullivan and Flanagan's (2003) model for harm assessment to advocate in favour of a three tiered framework to assess virtual harm. I argue, as socio-legal scholars advocate, that this framework should include an assessment of subjective experience of harm. However, I depart from single-tiered frameworks to suggest that harm assessment should also consider how violations are perceived and given meaning within the context of particular communities and subcultures, and, additionally, authorial intent.Finally, I consider how notions of "the virtual" can impact how users perceive and make meaning of fantasy and reality. I highlight that users in my dataset tend to perceive virtual spaces as playful or fantastical, and are consequently less likely to perceive virtual harms as legitimately harmful. To account for these perceptions, I conclude by suggesting that virtual spaces can be theorized as an extension of Huizinga's (1938) "magic circle", adding a final layer of complexity to my more nuanced ontology of virtual harm.
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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 | 0.067 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.018 | 0.161 |
| Scholarly communication | 0.029 | 0.046 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".