Ten Years of CHI PLAY: A Panel on the Past, Present, and Future of the Conference
Bibliographic record
Abstract
The community-building efforts of many volunteers has grown the games and play community within SIGCHI into a vibrant and active group of researchers. Our dedicated conference—CHI PLAY—started in 2014 with the goals of being an inclusive, diverse, welcoming, and playful community that publishes the highest quality research papers and provides structured opportunities for networking, feedback, mentoring, and collaborative knowledge generation. In this panel, a group of CHI PLAY former, current, and future leaders reflects on the formation and care of the CHI PLAY conference over the last decade and shares their visions for the evolution of CHI PLAY over the next decade.
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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".