Bibliographic record
Abstract
Conventional gameplay requires that players follow rules and aim for goals. Unconventional gameplay does not: cheats ignore rules, triflers ignore goals, and spoilsports ignore both. These unconventional modes of gameplay seem to be perennial preoccupations for those scholars concerned with the relationship between games and life, or with a political reading of games. Since the field’s inception, game studies scholars have conceived of these unconventional modes of play in metonymical or indexical terms, turning cheats, triflers, and spoilsports into different kinds of social actors who trouble the laws and conventions of society in different ways. Setting aside the comparatively straightforward rule breaking of the cheat, in this chapter I review four books – John Huizinga’s Homo Ludens, Bernard Suits’ The Grasshopper, James Carse’s Finite and Infinite Games, and McKenzie Wark’s Gamer Theory – that cast the trifler and spoilsport as complicated agents of social change. By demonstrating the affinity between these texts, I resituate the figures of the trifler and the spoilsport in the field of game studies and identify a normative and philosophical framework for thinking about games that is grounded in a radical critique of the present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".