An accessibility-first approach to online course readers
Bibliographic record
Abstract
Purpose The purpose of this study was to develop a cost- and labor-efficient method for a small library to produce and deliver accessible course reading packages. Design/methodology/approach Working with approximately 25 courses and instructors in the Fall 2017 semester – including courses in Equity Studies and Disability Studies – the authors produced an inventory of assigned readings and an assessment of the accessibility of scanned texts that were currently being used. Based on this initial inventory, they developed new workflows for providing accessible readings to students that overcame the most common shortcomings and deficiencies. Findings This project established that it is possible for a small library to produce high-quality accessible course readings and that a PDF file is the most appropriate format for providing accessible scanned readings in an online course reader environment. Practical implications This project developed a workflow for producing texts that are designed from the perspective of universal access – that is, all students can engage with these texts without requiring the intervention of accessibility-services-style departments. Originality/value Canadian academic institutions are required to provide accessible texts upon request, a process which relies on students to identify required readings, sign up for specialized services and be comfortable disclosing and discussing their specialized needs. The process developed in this project builds upon a conception of equitable access as being a core principle and strives to create accessible readings as a default rather than as the result of an external request. This case study can be used as an example for institutions – especially small libraries – that are interested in developing a proactive approach to creating accessible readings and course packs.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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".