Dangerous Play in an Age of Technofinance: From the GameStop Hunger Games to the Capitol Hill Jamboree
Bibliographic record
Abstract
In this paper we explore one dimension of the contemporary cultural politics that gives rise to reactionary formations and movements: the desire for a kind of dangerous play within a financialized world where most people feel trapped in a game they can’t win. We take up three interwoven phenomena from the recent past in the United States, though with implications beyond that context: (a) the GameStop stock-buying frenzy of early 202l, (b) the storming of the US Capitol building on January 6 of that year, and (c) the dramatic rise in popularity of the QAnon conspiracy fantasy that appeared in 2017 and gained significant influence since. By locating these complex participatory phenomena in the context of digitized financialization characterized by gamification, alienation and profound inequalities, we supplement efforts to understand today’s reactionary imagination. We argue that all three might be seen as, in part, forms of dangerous play that both emerge from, rebel against and also, contradictorily, help to reproduce or entrench dominant inequalities. Hence those who wish to counter these reactionary tendencies or propose more radical responses cannot limit themselves to critique; they must also contend with what animates these forms of alienated play.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".