Buying Time: Capitalist Temporalities in Animal Crossing: Pocket Camp
Bibliographic record
Abstract
In November 2017, Nintendo released Animal Crossing: Pocket Camp (Nintendo 2017) for iOS and Android devices. At first blush, the game is much like previous instalments in the series. The player character finds themselves as a new denizen of a rural space populated by sentient animals that all have wants and offer rewards for those that satisfy those wants. However, the conversion of Animal Crossing from console game to mobile game was not without its major changes. A free-to-play game par excellence, Pocket Camp introduces Leaf Tokens, a separate currency from bells which can be bought with real money. Leaf Tokens can be used to buy certain in-game objects but, for the most part, are used to eliminate instances of waiting in the game, which stands in direct opposition to the series’ apparent valorization of slower, simpler living. Through a discussion of this translation of Animal Crossing’s mechanics and values into the mobile game genre, Pocket Camp is shown to gamify the capitalist monetization of time. In the face of this reality, the paper concludes examining the role of the player as a critical actor within this system and suggests that, far from being a passive victim of the game’s capitalist logics, one might engage with the game in subversive ways that articulate a virtual refusal of virtual labour and an instance of what the author has taken to calling radical slowness.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".