Bibliographic record
Abstract
This highly speculative paper proposes the term playgrom to identify an idiom of playfully cruel fascistic violence that emerges from, is shaped by, and also exceeds gamified financialized capitalism. Like the antisemitic pogroms of late-Tsarist Russia and subsequent acts of racist terrorism there and elsewhere, the playgrom appears to be a form of spontaneous, unsanctioned majoritarian mob violence against minorities. But we must look to the deeper roots of such phenomena in both dominant ideologies and collapsing socioeconomic systems. I draw on the examples of the 2014-15 Gamergate online decentralized anti-feminist swarming campaign and the 2019 Christchurch white supremacist massacre as examples of the playgrom. As others have illustrated, these acts of mass violence, which I will characterize as fascistic, were gamified, and drew on gaming themes, tropes and communities for their vitality. But I also propose that, to fully understand these phenomena, we must also contextualize them in the current moment of gamified capitalism, and so I propose approaching the playgrom as a form of deep, dark playbor.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".