PACMHCI V8, CHI PLAY, October 2024 Editorial
Bibliographic record
Abstract
We are excited to present PACM HCI's 2024 issue on games and play research, which contains 63 original and highly relevant articles covering the entire spectrum of HCI games research. As in the previous year, we used three recommendations ' 'Accept with Minor Revisions', 'Revise and Resubmit', and 'Reject.' For papers that were accepted with minor revisions, the second round of reviews was a 'light-touch' process in which the editorial board members checked the revisions, whereas papers that received Revise and Resubmit received another full round of external reviews with the same reviewers in most cases, and were also subjected to a final selection process. This second revision was treated as a real resubmission, which meant that it was given full consideration, but no guarantee or preferential treatment towards acceptance; a share of the revised papers was ultimately rejected. We would like to acknowledge the efforts that our community has made in adapting to this new process, working together with the submitting authors to achieve high-quality scholarship. The track editorial board consisted of 32 members and a total of 230 external reviewers from around the world ensured a high-quality review process. After the first round of submissions, each paper was handled by a primary track editorial board member who received reviews from a second track editorial board member and two external reviewers so that each submission received at least three high-quality reviews. After reviews were completed and checked for quality, the primary initiated discussion amongst the reviewers and came up with a preliminary recommendation (accept, between accept and revise and resubmit, revise and resubmit, between revise and resubmit and reject, reject). Papers that were in the middle three categories were then discussed in two synchronous virtual track editorial board meetings that took place over two consecutive days. The inclusion of the board meeting at this stage was a new addition to the review process last year. We included it to better calibrate decision making across the committee at this critical point in the review process, ensure consistency in outcomes, promote reflection and consideration at this stage in the process, and mentor newer editorial board members in the review process. We strove for a diverse program approaching games research from a variety of perspectives, including design, engineering, psychology, computer and data science while at the same time only accepting high-quality pieces of work. In this issue, we observe the following distribution of primary contributions: 42.9% of papers self-classify as using qualitative methods, 11.1% use quantitative methods, and 20.6% use mixed methods. Additionally, 3.2% of papers present design artifacts and 6.3% present technical artifacts. Finally, 4.8% of papers employ meta-research methods, 6.3% of papers present a new methodological approach, and 4.8% of papers contribute to the development and validation of theory. All articles in this issue were invited to present at the ACM CHI PLAY 2024 conference. We thank our dedicated team of track editorial board members, external reviewers, and paper authors, who continue to support the new review process and timeline, contributing to the maturation and growth of the PACM HCI games and play research community, and resulting in this issue of 63 exciting and highly relevant articles. This issue's 63 papers reflect the importance of play in our everyday lives, comment on recent trends and technical developments relevant to games and play, and also represent a significant effort of our community in coming together to produce a collection of research that highlights the multifaceted value of play.
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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.222 | 0.160 |
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".