Playing with Genre: User-Generated Game Design in LittleBigPlanet 2
Bibliographic record
Abstract
Although computer and video games are traditionally understood as interactive experiences designed by professional developers, the increasingly social nature of these interactions means that players often become involved in the design process as well. In particular, games that include developer kits and level editors enable a form of participatory culture in which players directly perform user-generated game design—creating their own game rules and challenges for other players—by means of “modding” or other design activities. We explore how players perform user-generated game design by analyzing player-designed levels in the popular game LittleBigPlanet 2,using game analysis to consider the design of selected levels and how those levels are presented to and viewed by other players. We describe how players create levels that build on the game's existing genre, but also manipulate this genre to emphasize their own interpretations of what it means to play a video game. This study contributes an initial exploration of a form of end-user design that is of growing importance in video games, with potential implications for the design of future games and other participatory systems.
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 | 0.000 |
| 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 | 0.001 |
| 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".