Being played in everyday life: Massive data collection on mobile games as part of ludocapitalist surveillance dispositif
Bibliographic record
Abstract
Surveillance in videogames is a well-known phenomenon. Designated as the fastest-growing sector of the videogame industry, mobile games – particularly free-to-play games – capitalise substantially on the collection of user data. Based on the promise of offering personalised gaming and advertising experiences, a vast quantity of data, including personal identifier and geolocation data, is acquired through players’ mobile devices. While the information obtained may appear fragmented or invisible to players, they are consolidated in the hands of data brokers, resulting in a very lucrative economic sector. From this perspective, the practice of the mobile game, although innocuous at first consideration, raises essential ethical questions regarding the ludocapitalist surveillance dispositif established by this industry. In this chapter, we seek to problematise everyday surveillance in mobile gaming, explain how the videogame and marketing industries operate it, and examine gamers’ (“ordinary” citizens) involvement in the banalisation of this massive data gathering.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".