Proceedings of the 2016 ACM Conference Companion Publication on Designing Interactive Systems (DIS 2016) [Volume 2]
Bibliographic record
Abstract
Welcome to the southern hemisphere, welcome to Australia, welcome to Brisbane, welcome to the picturesque Gardens Point campus of Queensland University of Technology (QUT), and welcome to the 11th ACM SIGCHI Designing Interactive Systems (DIS'16) conference! Since 1995, the (so far1) biennial Conference on Designing Interactive Systems (DIS) is the premier, international arena where designers, artists, psychologists, user experience researchers, systems engineers, and many more, come together to debate and shape the future of interactive systems design and practice. DIS is owned by the ACM Special Interest Group on Computer- Human Interaction (SIGCHI). In the past, DIS has been held in Ann Arbor (1995), Amsterdam (1997), New York (2000), London (2002), Boston (2004), State College (2006), Cape Town (2008), Aarhus (2010), Newcastle (2012), and Vancouver (2014). The DIS'16 conference schedule comprises two days of pre-conference workshops and a doctoral consortium as well as three main conference days packed with peer reviewed paper presentations, demos, panel discussions, and three invited keynote speakers. The social program includes the welcome reception on Sunday night, the DIS Experience Night on Monday night, the DIS conference dinner on Tuesday night, and a post-conference after party on Wednesday night. And for all those who still haven't had enough, the QUT School of Design has added a great satellite event held in conjunction with DIS'16: the inaugural Queensland Design Policy Summit, a full day event on Thursday 9 June, dedicated to bringing together thought leaders and policy makers across design, business, science, education, and citymaking in a dialogue to canvass the merits of resurrecting a revised, fresh, and bold Queensland Design Policy. The DIS'16 theme of fuse is about exploring the range of new possibilities along the human and technology spectrum -- the blurring of any clear divides between analog and digital, atoms and bits, materiality and virtuality, art and design, academy and industry. From mobiles to wearables, bearables, and even injectables, technology is finding its way to be carried by us, on by, and inside us. At the same time do we welcome more and more robots and humanoids into our lives -- from Furby, Roomba, Siri, to Jibo. Are we witnessing a fusion of the cyborg with the matrix? What does the future of interactive systems hold? We hope to advance these questions at DIS'16. I am very pleased that we were able to curate and assemble a stellar program, including three invited keynote presentations by Carlo Ratti (MIT), Natalie Jeremijenko (New York University), and Stelarc (Curtin University of Technology). I am sure they will stimulate our thinking particularly at the intersection of design and cities, the environment, and the human body.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.313 | 0.173 |
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".