Bibliographic record
Abstract
Gambling games are composed of risk and contingency - the gambler stakes their bet on the spin or a reel or a roulette wheel completely dependent on forces outside of their control, uncertain of the outcome. This potent combination is not only being used to fuel the nearly $500 billion USD global gambling industry, but also to organise the current app economy. Digital platforms, their complementors, and their users are brought together by risk and contingency into a dynamic political economy, with the platform accruing the most advantage (Poell et al., 2021). Unpacking these unequal and sometimes precarious relations requires studying a “representative commodity” (Kline et al., 2003). Social casino apps, a niche, but still significant digital game commodity, embody how risk and contingency manifests in the app economy (Nieborg & Poell, 2018; Zittrain, 2008). In particular, when other industries interface with digital platforms, they become subject to their institutional imperatives (Gorwa, 2019). Social casino apps are representative of how platforms have been able to influence and shape even niche genres of digital leisure, but also the constraints and resistance to these techniques. In this paper, as a political economist of communication, I conduct a structural and critical analysis of the social casino industry, using institutional analysis as my methodology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".