Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Bibliographic record
Abstract
ASSETS 2005 is the seventh in the ACM's series of conferences sponsored by SIGACCESS (formerly SIGCAPH). As in the past, ASSETS 2005 focuses its attention on topics related to design, development, and application of computer-based methodologies to achieve the objectives of universal access, with special attention to the needs of people of al ages and with different capabilities. The ASSETS series of conferences is aimed at providing a technical forum for presenting and disseminating innovative research results that cover either (1) applications of computing and information technologies to provide assistive systems to persons with special needs, or (2) investigation of computing technologies and their use by persons with disabilities. This year, the conference opened with a plenary address by Dr. Clayton Lewis, Professor of Computer Science at the University of Colorado at Boulder. His presentation was titled "Bridges for the Mind: Opportunities for Research on Cognitive Disabilities. ASSETS 2005 is the first event of this conference series to be presented on an annual basis - the previous ASSETS conferences have been held bi-annually. The change to an annual event is in response to the growth of the field and the increasing interest in a more frequent technical venue and discussion forum. As in previous years, the conference is organized in such a way to promote an open forum where researchers, practitioners, educators, and students can present their ideas as formal papers, posters, demonstrations, as well as engage in dialogue, sharing experiences, objectives, accomplishments and different perspectives. In this tradition, ASSETS 2005 is developed as a single-track conference, where formal presentations, interactive sessions, panels, and social events alternate throughout the conference program. The technical program is composed of 23 regular papers, selected by the international program committee out of a total of 60 submissions. The accepted submissions represent the work of 58 authors from Canada, Chile, India, Japan, Sweden, UK, and the USA. The papers are organized in six thematic sessions. The program also includes two sessions dedicated to the presentation of cutting-edge results and work-in-progress, in the form of demos and posters. Posters and demos were submitted in response to a separate call for papers, which generated 30 submissions, of which 21 were selected for presentation. A 2-page abstract for each poster and demo appears in these proceedings. Both technical papers and poster/demo papers were subjected to a competitive peer-review process, ensuring ensured that the papers included in these proceedings represent the state-of-the-art in the field. The program is completed by a panel and the doctoral consortium. The panel brings together researchers, government, and industry representatives to discuss the duality between universal access and assistive technologies. The doctoral consortium, chaired by Julie Jacko, offers an opportunity to doctoral students to present their ideas to both peers and a selected pool of experts. In addition, the main conference includes a session dedicated to the doctoral consortium, providing an opportunity for participants to share their research with the entire ASSETS audience. One doctoral student, selected by the doctoral consortium committee, will present the closing plenary for ASSETS 2005.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.186 | 0.087 |
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".