Productive play: The shift from responsible consumption to responsible production
Bibliographic record
Abstract
Regulatory approaches to games are organized by boundaries between game/not-game, game/gambling game, skilled/unskilled play, consumption/production. Perhaps more importantly, moral justifications for regulating gambling (and condemning digital games) are rooted in the idea that they consume our time and wages but give little in return. This article uses two case studies to show how these boundaries and justifications are now perforated and reconfigured by digital mediation. The case study of Daily Fantasy Sports (DFS) illustrates a contemporary challenge to rigid dichotomies between game/not game, skilled/unskilled play, and game/gambling game, demonstrating how regulation becomes deterritorialized as gambling moves out of state-regulated physical casinos and takes the form of networked, digital games. Our second case study of Pokémon Go approaches regulation from a different direction, complicating the rigid dichotomy between production/consumption in online networked play. We show how play is increasingly realized as productive in economic, social, physical, subjective and analytic registers, while at the same time, it is driven by gambling design imperatives, such as extending time-on-device. Pokémon Go exemplifies analytic productivity, a term we use to refer to the production of data flows that can be leveraged for a wide variety of purposes, including to predict, shape, and channel the behaviour of player populations, thereby generating multiple streams of revenue. Ultimately, both cases illustrate how digital games and gambling increasingly blur into each other, complicating the regulatory landscape.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.092 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".