Tool Design Jam
Bibliographic record
Abstract
In both industry and academia, software tools are essential for games user research (GUR) in order to collect, integrate, analyze and report on games and players' data. GUR datasets are becoming more and more complex, detailed and multifaceted. Hence, tools are necessary to efficiently handle data. This one-day workshop explores the vast spectrum of tools used and created by current GUR researchers and provides a platform of discussion for advancing the development of such tools. This workshop will facilitate intersections from user researchers with diverse epistemologies, as well as from both academia and the industry, in an interactive Design Jam activity to collaboratively design future-proof GUR tools. The immediate outcome of the workshop is twofold: to collectively establish state-of-the-art tool design guidelines, and to archive the papers and discussions, which will extend the conversations and relationships beyond the workshop. Moreover, the long-term outcome will be the start of a community that focuses on creating better tools to aid the study of player experiences.
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 | 0.030 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.085 | 0.026 |
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".