Digital Accessibility as a Business Practice : Essential Skills for Business Leaders
Bibliographic record
Abstract
Most business leaders would agree that reaching the broadest audience is good for a business’s bottom line. A good portion of that audience will be people with disabilities. How, though, would an organization go about ensuring it is as accessible as it can be to all its potential clients or customers, including people with disabilities? This resource has been created to answer this question and to demystify “digital accessibility” as a business practice. It brings together all the pieces of the digital accessibility picture and provides strategies and resources that will help make digital accessibility a part of an organization’s business culture. This resource is an adaptation of the massive open online course (MOOC) of the same name, developed through The G. Raymond Chang School of Continuing Education at Ryerson University and offered through the Canvas Network. To see when the course will be offered next, check the course website. Though the resource originates in Ontario, Canada, and includes some discussion of the Accessibility for Ontarians with Disabilities Act (AODA), the content will be relevant to a global audience. Accessibility as it applies to AODA and Ontarians applies equally in other jurisdictions, albeit perhaps in some cases without the motivation of the law to enforce it as a requirement. Many are watching Ontario as it rolls out its 20-year plan to make the province the most accessible jurisdiction in the world. Though the learning materials here are aimed at educating business leaders and managers about digital accessibility as a business practice, it will be of interest to anyone who wants to understand organizational culture in general and how digital accessibility fits into that culture. What you’ll learn here goes well beyond accommodating people with disabilities or adhering to the law. It is about improving your bottom line and ensuring your business or organization is able to serve its whole audience — not just those who are able bodied or using the latest technology, but also those from the margins of society, who are often overlooked by the mainstream. Being a good “corporate citizen” and “doing the right thing” are phrases often used to justify making an effort to remove potential barriers to goods and services, but it’s more than that. The business arguments for accessibility are many. They are about reaching the broadest audience possible. People with disabilities have family and friends, who will go elsewhere if together they are unable to effectively access your business’s website or digital content. When you consider that people with disabilities make up nearly 15% of the population (WHO), and when you include their mothers and fathers, brothers and sisters, aunts and uncles, and more, that number can reach 50% of the population who are affected by disability in one way or another. Most businesses would have a hard time justifying serving only 50% of their potential customer base. The bottom line: Digital accessibility is good for business.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.017 | 0.068 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".