Access to information and instructional technologies in higher education I: Disability service providers’ perspective
Bibliographic record
Abstract
This is an applied companion to our empirical article elsewhere in this issue (Fichten et al., in press) on technological needs and concerns of Canadian junior/community college- and university-based disability service providers. Here, we provide highlights of our findings as well as timely, practical recommendations to disability service providers about ensuring access to the growing array of information and instructional technologies on campus. The objective is to provide (a) an overview of the emerging landscape of information and instructional technologies appearing on campus, (b) campus-based disability service providers ’ views about these and how these relate to adaptive technologies, and (c) suggestions about how to be proactive on campus so that information and instructional technologies are accessible to all students, particularly those with disabilities. The underlying premise of this article is that infor-mation and instructional technologies are part of the everyday lives of college and university students now, and for the foreseeable future. Whether it is registering via the Web for a semester’s worth of courses, taking a university degree fully on-line, conducting complex phys-ics experiments using a computer-based simulation tool, or downloading assignments from a professor’s Web site, students are bombarded with multiple opportunities to
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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".