Spinning is winning: Social casino apps and the platformization of gamble-play
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social casino apps are an emergent genre in the app economy that sits at the intersection of three different industries: casino gambling, freemium mobile games, and social media platforms. This institutional position has implications for the social casino app’s political economy and culture of consumption. We argue that social casino apps are representative of a broader casualization of risk that has taken hold in a platform society. By combining the uncertainty and chance associated with gambling with the interruptibility, informality, and modularity of free-to-play mobile games, social casino apps offer complete contingency in how they are designed and played. Game progression and social networking features are used to normalize the relationship between the consumer of social casino apps and the contingency of their desired form of play. As a result, the experience of risk is no longer restricted to the casino floor and in fact becomes a part of one’s daily routine. This casualization of risk marks the next adaptation of the contingent cultural commodity, where nothing is guaranteed and everything is subject to chance.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it