Games and Play SIG: Connecting Games Research to the Broader HCI Context
Bibliographic record
Abstract
Research on games and play has been present at CHI since the first conference in 1982. The community-building efforts of many volunteers has grown the games and play community within SIGCHI into a vibrant and active group of researchers, with a dedicated conference (CHI PLAY) that publishes its full papers in the GAMES track of the ACM PACMHCI journal. However, we there are members of the larger HCI community whose research and practice intersects with games and play—in topics such as emerging technologies; VR/AR/XR; theories of motivation, experience, and personality; metaverse; livestreaming; fan, and spectator communities; accessibility; and serious games—who may never have attended a games-specific conference. The purpose of this SIG is to offer a lightweight opportunity for CHI attendees to connect with the games and play research community. Our aim is to meet as a community, and to connect with HCI researchers who have not traditionally seen their work as part of games and play for networking and bi-directional idea exchange.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".