NTID International Symposium on Technology and Deaf Education
Bibliographic record
Abstract
The International Symposium on Technology and Deaf Education at the National Technical Institute for the Deaf (NTID) at Rochester Institute of Technology is a bi-yearly (occurring once every two years) conference that attracts professionals, educators, technologists, and researchers who are interested in, developing for, and affected by technology for the education of deaf and hard of hearing students. This mixture of professionals leads to many interesting discussions, presentations, and exhibits with the purpose of dissemination of full research projects and best practices. This year the symposium, June 21--23, 2010, accepted 64 papers each with presentations 45 minutes long, 15 minutes of which were devoted to question and answer, and discussion. Presenters were chosen to reflect the current issues and solutions in both educational and access technologies. The symposium is also a great venue to share works-in-progress, current trends, and state of the art technology shown by 28 posters and 11 commercial exhibits. Over 300 people attended from 17 different countries including presentations by participants from Japan, Thailand, Canada, China, Czech Republic, Russia, Philippines, Poland, and Rwanda.
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 | 0.002 | 0.003 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.042 |
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".