Bibliographic record
Abstract
Many sports video games contain elements such as running or throwing that are based on real-world physical activities, but the translation of these activities to game controllers means that the original physicality is lost. This results in games where players have limited opportunity to improve their physical skills, where there is little differentiation in people's physical abilities, and where skills do not change over the course of a game. To explore ways of adding these elements back into sports games, we developed two games with small-scale physical controls for running and throwing -- one game was a simple running race, and one was a team-based handball-style game called Jelly Polo. In two studies (three track-and-field tournaments for the running game, and a four-week league for Jelly Polo), we observed the effects of physical controls on gameplay. Our studies showed that the physical controls enabled substantial individual differences in running and passing skill, allowed people to increase their expertise over time, and led to fatigue-based changes in performance during a game. Physical controls increased the games' challenge, complexity, and unpredictability, and dramatically improved player interest, expressiveness, and enjoyment. Our work shows that game designers should consider the idea of "exertion in the small" as a way to improve play experience in games based on physical activities.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".