Assistive Technologies used in Canadian University Libraries for the Visually Impaired
Bibliographic record
Abstract
Information has always been a catalyst for societal improvements. Libraries play a crucial role in ensuring equitable access to information resources. Enhanced accessibility helps in establishing an atmosphere that supports and promotes inclusive education. Information and resource access is quite a challenge for visually impaired people. The needs of people with varying levels of visual impairment differ in their range and magnitude. The use of assistive technologies greatly aids in closing this gap. Resource and accessibility provisions should be modified to meet the needs of visually impaired users, who need specialized equipment to access traditional and modern technology-based information resources. University libraries worldwide have used many assistive technologies to make it easier for patrons who are blind to access information resources. This research is a preliminary examination of the Dalhousie University Library's (2021) website for the assistive technology tool used to support visually impaired patrons and students. The research examines the assistive technologies used by Canadian university libraries similar to Dalhousie Libraries and offers potential implementation recommendations for Dalhousie Libraries to increase their arsenal of assistive technologies to support and promote access to visually impaired users
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".