A chronology of SIGCHI conferences
Bibliographic record
Abstract
research-article Open AccessA chronology of SIGCHI conferences: 1983 to 2022 Authors: Neha Kumar Georgia Institute of Technology Georgia Institute of TechnologyView Profile , Julie A. Adams Oregon State University Oregon State UniversityView Profile , Bill Buxton Microsoft Research Microsoft ResearchView Profile , Linda Candy ArtworksrActive ArtworksrActiveView Profile , Pablo Cesar Centrum Wiskunde & Informatica and Delft University of Technology Centrum Wiskunde & Informatica and Delft University of TechnologyView Profile , Leigh Clark Bold Insight UK Bold Insight UKView Profile , Benjamin R. Cowan University College Dublin University College DublinView Profile , Anind Dey Carnegie Mellon University Carnegie Mellon UniversityView Profile , Phoebe O. Toups Dugas New Mexico State University New Mexico State UniversityView Profile , Ernest Edmonds De Montfort University De Montfort UniversityView Profile , Michael A. Goodrich Brigham Young University Brigham Young UniversityView Profile , Mark Green Ontario Tech University Ontario Tech UniversityView Profile , Jonathan Grudin Microsoft and University of Washington Microsoft and University of WashingtonView Profile , Yoshifumi Kitamura Tohoku University Tohoku UniversityView Profile , Joe Konstan University of Minnesota University of MinnesotaView Profile , Celine Latulipe University of Manitoba University of ManitobaView Profile , Minha Lee Eindhoven University of Technology Eindhoven University of TechnologyView Profile , Tom Malone Massachusetts Institute of Technology Massachusetts Institute of TechnologyView Profile , Regan Mandryk University of Saskatchewan University of SaskatchewanView Profile , Panos Markopoulos Eindhoven University of Technology Eindhoven University of TechnologyView Profile , Michael Muller IBM Research IBM ResearchView Profile , Lennart Nacke University of Waterloo University of WaterlooView Profile , Yukiko Nakano Seikei University Seikei UniversityView Profile , Marianna Obrist University College London University College LondonView Profile , Martin Porcheron Swansea University Swansea UniversityView Profile , Aleksandra Sarcevic Drexel University Drexel UniversityView Profile , Johannes Schöning University of St.Gallen University of St.GallenView Profile , Stacey Scott University of Guelph and University of Waterloo University of Guelph and University of WaterlooView Profile , Bonita Sharif University of Nebraska - Lincoln University of Nebraska - LincolnView Profile , Frank Steinicke University of Hamburg University of HamburgView Profile , Simone Stumpf University of Glasgow University of GlasgowView Profile , Edward Tse NUITEQ NUITEQView Profile , Vinoba Vinayagamoorthy Samsung SamsungView Profile Authors Info & Claims InteractionsVolume 29Issue 6November - December 2022 pp 34–41https://doi.org/10.1145/3568732Published:03 November 2022Publication History 0citation707DownloadsMetricsTotal Citations0Total Downloads707Last 12 Months707Last 6 weeks47 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.070 | 0.047 |
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".