Bibliographic record
Abstract
Welcome to the 7th International Workshop on Games and Software Engineering (GAS 2023), held in conjunction with the 45th ACM/IEEE International Conference on Software Engineering (ICSE 2023).GAS is an annual event gathering researchers and practitioners interested in sharing and advancing game engineering and software engineering techniques.GAS explores how the growing adoption of gameful elements in various contexts can make the design and development of new technology increasingly complex, and provides a forum to explore these issues that crosscut the software engineering and games development communities.The goal of this one day workshop is to bring together interdisciplinary researchers and practitioners to discuss emerging and new research trends, challenges, costs, and benefits for entertainment games, serious games, and the gamification of traditional (non-game) applications and activities.The accepted workshop papers span topics from videogame software architecture, game-based software engineering education, automatic assessment of game balance, and domain specific languages for game progression design, to surveys of current game developer practices.In addition to these, the workshop program includes a panel discussion on the parallels and interactions between game design and software engineering education programs.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.120 | 0.103 |
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".