Online databases and the research experience for university students with print disabilities
Bibliographic record
Abstract
Purpose The purpose of this paper is to present findings of a study which examined the searching experience of university students with a print disability and their use of screen reading software to navigate three proprietary databases. Design/methodology/approach Participants completed a series of tasks in three different online databases using their screen‐reading software. Screen‐recording software provided video and audio documentation of the process. Survey data were collected pre‐ and post‐study as well as after each database search session. Findings The paper provides insights on the information‐seeking behavior of students with print disabilities as well as the barriers encountered while navigating online databases using screen‐reading software. Research limitations/implications The study focuses only on a small sample of university students with print disabilities and therefore lacks a control group of non‐print‐disabled students against which the results could be measured. Practical implications Database vendors are aware of the barriers their databases pose for users of screen readers. It is in the best interest of vendors to assist libraries in promoting the accessible features that already exist in their databases. Libraries can assist students by providing database instruction tailored to users of screen readers and by assisting database vendors in usability feedback and in marketing options. Originality/value Participants consisted of students with learning, visual and mobility disabilities and who were native users of screen‐reading software. There is a lack of research on the intersection of databases design and its impacts on the information literacy skills of students with print disabilities. This paper provides some insights on the first step in the information‐seeking process (gathering information) by students with a print disability and the barriers encountered.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".