The Musings of ‘Evil Bastards’: Perspectives from Social Casino Game Professionals
Bibliographic record
Abstract
Technology has blurred the lines between gambling and gaming. While the convergence can be witnessed on many different levels, social casino games on social networking sites and mobile apps illustrate just one example. Much of what we currently know about social casino games focuses on player behaviour, with little understanding about this genre from the perspective of social game professionals. This paper aims to fill the gap in our understanding of social casino games through interviews with the professionals who design them. In-depth interviews were conducted with 14 professionals from the social casino games industry. Interviews were analyzed using thematic analysis. Findings illustrate tensions that exist between the two fields of gambling and gaming; however, both are trying to separate themselves from the stigmatized ‘dirty secret’ that is gambling. Further, as a result of social casino games residing, for the most part, in an unregulated ‘grey area,’ findings illustrate the ethical struggle felt by social casino game professionals. This convergence has significant consequences, not only for players, but for game developers, designers, and researchers, and highlights the importance of game designer education.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.024 | 0.039 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| 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".