‘The biggest legal battle in UK casino history’: Processes and politics of ‘cheating’ in sociotechnical networks
Bibliographic record
Abstract
Previous literature on cheating has focused on defining the concept, assigning responsibility to individual players, collaborative social processes or technical faults in a game's rules. By contrast, this paper applies an actor-network perspective to understanding 'cheating' in games, and explores how the concept is rhetorically effective in sociotechnical controversies. The article identifies human and nonhuman actors whose interests and properties were translated in a case study of 'edge sorting' - identifying minor but crucial differences in tessellated patterns on the backs of playing cards, and using these to estimate their values. In the ensuing legal controversy, the defending actors - casinos - retranslated the interests of actors to position edge sorting as cheating. This allowed the casinos to emerge victorious in a legal battle over almost twenty million dollars. Analyzing this dispute shows that cheating is both sociotechnically complex as an act and an extremely powerful rhetorical tool for actors seeking to prevent changes to previously-established networks. Science and Technology Studies (STS) offers a rich toolkit for examining cheating, but in addition the cheating discourse may be valuable to STS, enlarging our repertoire of actor strategies relevant to sociotechnical disputes.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.027 | 0.051 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".