Bibliographic record
Abstract
A couple of months ago, we were in Rome for the fourth edition of the Game and Learning Alliance – Gala Conf 2015 (http://www.galaconf.org). The conference has been a success both for number of participants and for quality of the presented works, leading to interesting discussions and exchanges.Best papers have been selected and authors are now preparing the extended versions of their works, that will be reviewed for an upcoming special issue on the International Journal of Serious Games.In a ceremony during the conference, best serious games were awarded the SGS awards, both in the category business and academy.Workshops, particularly in the fields of healthcare, intelligent transportation and management, have revealed interesting trends, especially in the mentioned application fields.During the conference, the annual general assembly of the Serious Games Society was held. Among other decisions, the assembly selected the venue for Gala Conf 2016, that will take place in Utrecht, the Netetherlands, on December 5-7, 2016. Also, in collaboration with the Laval University, SGS is organizing Gala Quebec, October 11-12 2016 in Ville de Quebec, Canada.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.055 | 0.047 |
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".