Bibliographic record
Abstract
Welcome to this issue of the Proceedings of the ACM on Human-Computer Interaction, which will focus on contributions from the research community Engineering Interactive Computing Systems (EICS). This diverse research community explores the methods, processes, techniques and tools that support specifying, designing, developing, deploying and verifying interactive systems. Building interactive systems is a multifaceted and challenging activity, involving a plethora of different actors and roles. This is particularly true in the domain of HCI, where we continuously push the edge of what is possible, where there is a crucial need for adequate processes, tools and methods to build reliable, useful and usable systems that help people cope with the ever-increasing complexity of work and life. The contents of this issue on EICS is the sum of four separate rounds of submissions, evenly spaced from July 2017 through May 2018. In total, the rounds attracted a total of 81 submissions from Asia, Canada, Australia, Europe, Africa, and the United States. Promising submissions in a round that were not accepted were invited to resubmit to a subsequent round, and 6 of the papers appearing in this issue were accepted after at least one round of resubmission. In each round, papers were subject to a rigorous reviewing process where they were reviewed by two EICS senior editors, as well as external reviewers. At the conclusion of each round, a Virtual Committee meeting was held to discuss all of the papers and arrive at final decisions. Ultimately, 14 papers were accepted over all rounds. This issue exists because of the dedicated volunteer effort of 20 senior editors who handled two to four papers each round, and 115 expert reviewers to ensure high quality and insightful reviews for all papers in all rounds. Reviewers and committee members were kept constant as much as possible for papers that were submitted to multiple rounds. Senior members of the editorial group also helped shepherd some papers, reflecting the deep commitment of this research community. We are excited by the detailed and insightful work that resulted in this PACMHCI EICS issue and look forward to equally high quality submissions in subsequent submission cycles over the coming year. For those interested in this area, this group holds their next annual conference June 19-22, 2018 in Paris, France. That conference will provide many opportunities to share ideas with other researchers and practitioners from institutions around the world.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.372 | 0.254 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".