PACMHCI V5, ISS, November 2021 Editorial
Bibliographic record
Abstract
It is our great pleasure to welcome you to this issue of the Proceedings of the ACM on Human-Computer Interaction, the second to focus on the contributions from the research community Interactive Surfaces and Spaces (ISS). Interactive Surfaces and Spaces increasingly pervade our everyday life, appearing in various sizes, shapes, and application contexts, offering a rich variety of ways to interact. This diverse research community explores the design, development, and use of new and emerging interactive surface technologies and interactive spaces. The call for articles for this issue on ISS attracted 77 submissions, from all over the world. This issue has 23 papers, 4 submitted in February 2021 and 19 submitted in July 2021. After the winter round, 4 (total of 19 articles, 21.1%) articles were accepted and 5 (26.3%) articles required major revisions. After the summer round, 19 (total of 58 articles, 32.8%) articles were accepted, and 18 (31,0%) articles required major revisions. The editorial committee worked hard over the two iterations of the review process, winter and summer rounds, to arrive at final decisions. In total, counting both the winter and the summer rounds, 23 articles (total of 77 articles, 29.9%) were accepted. All authors of the accepted articles are invited to present at the ISS conference from November 14--17, 2021. This issue exists because of the dedicated volunteer effort of 31 senior editors who served as Associate Chairs (ACs), 105 expert reviewers in the winter round, and 206 expert reviewers in the summer round to ensure high quality and insightful reviews for all articles. Reviewers and committee members were kept constant for papers that submitted to both rounds. The Editorial Board is presented here: https://iss.acm.org/2021/organization/editorial_board
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".