Proceedings of the 2007 conference on Future Play - Future Play '07
Bibliographic record
Abstract
The Future Play Conference focuses on three main themes. The first theme, future game development, addresses academic research and emerging industry trends in the area of game technology and game design. The second theme, future game impacts and applications, includes academic research and emerging industry trends focused on designing games for learning, for gender, for serious purposes, and to impact society. Finally, the third theme, future game talent, is designed to provide a number of industry and academic perspectives on the knowledge, skills, and attitude it takes to excel in the games industry. Future Play addresses these issues through exciting and thought-provoking keynotes from leaders in academia and industry, peer-reviewed paper sessions, panel sessions (including academic and industry discussions), workshops (including design, technology, and career workshops), and exhibitions of posters, games, and the latest game technologies and supports from industry-leading vendors. The highlight of the games exhibition is a peer-reviewed competition of games in three categories: Indie Games, Serious Games, and Student Games. For Future Play 2007, Algoma University College teams up with the Ontario University Institute of Technology to give Future Play attendees the chance to interact with some of the most talented people in the gaming world today.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.288 | 0.167 |
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".