The Gamergate Social Network: Interpreting Transphobia and Alt-Right Hate Online
Bibliographic record
Abstract
This paper explores the relationship of transphobia and other forms of harassment found across the events of the Gamergate hate movement through the development of an interactive social network analysis. With the social network being derived from hundreds of events tagged by hand, special consideration is given to the positionality and biases of its authors and how they affected this specific interpretation of Gamergate events. Informed largely by the transgender perspective of its first author, this paper draws particular conclusions around the propagation of transphobia in online hate movements, such as its intersectionality with other ideological cornerstones of Gamergate. Cet article explore la relation entre la transphobie et d’autres formes de harcèlement observées lors des événements du mouvement de haine Gamergate à travers le développement d’une analyse de réseau social interactive. Avec le réseau social dérivé de centaines d’événements étiquetés à la main, une attention particulière est accordée à la position et aux biais de ses auteurs et à la manière dont ils ont influencé cette interprétation spécifique des événements de Gamergate. Principalement informé par la perspective transgenre de son premier auteur, cet article tire des conclusions particulières sur la propagation de la transphobie dans les mouvements de haine en ligne, comme son intersectionnalité avec d’autres piliers idéologiques de Gamergate.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".