Proceedings of the 3rd Australasian conference on Interactive entertainment
Bibliographic record
Abstract
On behalf of the organising committee, we would like to welcome you to the Joint International Conference on CyberGames and Interactive Entertainment 2006 (CGIE2006), held on 4-6 December 2006 in Western Australia. CGIE2006 is a special joint conference between CyberGames: International Conference and Exhibition on Games Research and Development, and Interactive Entertainment 2006: The Third Australasian Conference on Interactive Entertainment, and is being held in parallel at the same venue in Western Australia. We hope that this arrangement will result in synergy and collaboration between researchers and academics from the two areas. With tremendous support from the authors, program chairs, special sessions chairs, and technical program committee, we have been able to put together an outstanding program for the Joint Conference. There were many high quality submissions and we are pleased to accept 55 full papers, 1 abstract and 2 demos. These submissions have been received from all corners of the world, including Canada, France, Japan, New Zealand, Pakistan, Philippines, Singapore, Sweden, Taiwan, United Kingdom, and the United States of America, as well as Australia. All full paper submissions have been peer-reviewed by at least two international technical program committee members.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.202 | 0.076 |
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".