Computer technologies for postsecondary students with disabilities. [Booklet]. Montréal: Adaptech Project, Dawson College. Retrieved August 12, 2001 from http://omega.dawsoncollege.qc.ca/pubs/booklete.htm/booklete.htm
Bibliographic record
Abstract
In this companion paper to our scientific findings (Fichten, Asuncion, Barile, Fossey, & Robillard, 2001b) we focus on applied issues associated with providing computer related services to postsecondary students with disabilities. We use the results of our series of empirical studies of the needs and concerns of students with disabilities and individuals responsible for providing services to them. The goal is to target evolving issues, provide an up-to-date, user friendly list of resources, and make practical recommendations about what postsecondary personnel responsible for providing services to students with disabilities can do to facilitate access to computer and information technologies at their colleges and universities. Computer technologies are rapidly becoming a part of our professional, personal and academic lives. Because computer knowledge is a necessity for effective participation in the new North American economy, computer literacy and know-how are part of most postsecondary students ’ formal education. One need only look at North American colleges and universities to see this trend in action. North American college campuses are becoming increasingly “wired” and the technology is pervading all aspects of academic life (Bernstein, Caplan, Glover, 2001; EDUCAUSE Online Guide to Evaluating Information Technology on Campus, 2001). The integration of online courses and computer-mediated and web-based learning into curricula are top priorities at most universities and community/junior colleges. In parallel with this trend is evolution in the accessibility and affordability of both general use and adaptive computer
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.268 | 0.110 |
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".