Proceedings of the 10th Brazilian Symposium on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction
Bibliographic record
Abstract
We would like to extend our warmest welcome to all participants of this international joint conference. We celebrate the 10th edition of the Brazilian Symposium on Human Factors in Computing Systems (IHC) and the 5th edition of the bi-annual Latin American Conference on Human-Computer Interaction (CLIHC). We thus have been referring to this event as IHC+CLIHC 2011. We are proud to present a very rich program, which reflects much of the interests and endeavors of the HCI community in Latin America. Getting people involved, reviewing, discussing, critiquing, and ultimately selecting the contributions that are included in the program has been the work of an international team based in countries including Brazil, Mexico, United States, Colombia, Chile, Egypt, Spain, Guatemala, France, Italy and Canada. We received well over a hundred full and short papers, out of which we selected 32 full papers and 15 short papers, for an acceptance rate of about 33%, in all three languages of the conference: English, Portuguese and Spanish. The program of IHC+CLIHC 2011 is organized into thematic sessions that broadly reflect the main areas in which our community has been conducting research: Cultural issues, assistive technologies, usability, accessibility, multimodal interfaces, design issues, HCI education, and visualization and annotation techniques, among others. Technical sessions will be framed by keynote addresses by two distinguished speakers: Alex Reppening and Jesus Favela, who will share experience and insights on social thinking tools and user studies in pervasive healthcare research, respectively. The program also includes tutorial courses that will help newcomers to get up to speed and advanced attendees to keep abreast in areas such as usability evaluation, interface design and prototype construction. Specialized workshops will provide spaces for in-depth discussion of two key areas: Social networks and HCI instruction. Finally, we have had an enthusiastic response to this year's Student Competition. Out of 28 participating teams, the committee selected six in the Evaluation Track and three in the Design Track for the final stage, which will take place during the conference.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.149 | 0.039 |
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".