CHI '11 Extended Abstracts on Human Factors in Computing Systems
Bibliographic record
Abstract
Over the last year or so, we have been blessed with the challenge, the opportunity, and the distinct pleasure of organizing the CHI 2011 Conference on Human Factors in Computing Systems, the premier international conference for the field of human-computer interaction. CHI 2011 takes place in gorgeous, energetic, sophisticated Vancouver BC, city renowned for its innovation in entertainment, sustainability, accessibility, and inclusivity. The New York Times calls it, a liquid city, tomorrow city, equal parts India, China, England, France and the Pacific Northwest. Vancouver lays beautiful backdrop for our conference, which boasts nearly 30 years of wonderful work. Behind the success of the conference is our diverse community of faculty and students, of researchers and practitioners, of young and, well, also of experienced. It is community of designers, technologists, psychologists, social scientists, biologists, artists, engineers, anthropologists, musicians; the list goes on. Wherever we are, we are always community of near and far. Most impressively, ours is community that cares deeply about innovating, learning, sharing, and interacting; all with the common goal of using technology to shape the way people around the world live and play. Returning attendees will recognize the general conference format - 2 days of small intimate workshops, followed by 4 days of technical content, all surrounded by social and intellectual exchanges. In addition to the familiar venues that form the core of the conference, we have also arranged various special events, such an keynotes by Howard Rheingold and Ethan Zuckerman; invited talks by ACM SIGCHI award winners Terry Winograd, Larry Tesler, Alan Newell, and Clayton Lewis; an HCI museum exhibit hosted by Bill Buxton; and panel celebrating Stu Card's achievements and contributions to the field of HCI. With the record number of submissions and accepted content this year, we hope that you will utilize the print and electronic programs, but also the daily CHI Madness presentations that provide glimpse of the day ahead. In the interest of continuing to evolve the conference to best serve our needs, we will experiment with shorter talks this year (20 minute slots for long pieces of content and 10 for shorter ones) to infuse even more energy into the program. We will also have pretty full slate of social media applications to help you connect with other attendees and to provide you with the fullest experience possible.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.361 | 0.163 |
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".