Proceedings of the First Workshop on Design Patterns in Games
Bibliographic record
Abstract
Welcome to the first Workshop on Design Patterns in Games, held May 29, 2012 and co-located with the 7th International Conference on the Foundations of Digital Games. This workshop focuses on advancing design patterns as a means to formally describe a solution to a game design problem. Design pattern approaches have long been used in diverse fields such as architecture, software engineering, and interaction design. With the emergence of game scholarship, there has been interest in applying design patterns to aspects of game design. There are many potential benefits to design pattern approaches, including generation of frameworks for teaching and communicating about game design and practical usage in brainstorming ideas and tuning designs. Furthermore, deeper understanding of the patterns implicit in their games can help designers explore previously unused ideas and expectations of player behavior. This workshop features presentation of novel research papers followed by group discussion of emerging issues in the study of design patterns and games. We received ten paper submissions, of which we accepted six after a rigorous peer review process. Each paper received a minimum of three reviews. To insure high-quality contributions, each accepted paper was shepherded by a committee member before the submission of the final version. All accepted papers are available in the ACM Digital Library due to our in-cooperation agreement with SIGCHI.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".