A Story About Digitalization and Those Left Out : A quantitative study exploring the effect of digitalization on web accessibility.
Bibliographic record
Abstract
This thesis examines the influence of digitalization on web accessibility for people with\ndisabilities (PwD). The central research question addressed is: "How does digitalization\naffect web accessibility for people with disabilities?" The hypothesis proposed in this study\nis that web accessibility decreases as digitalization advances, irrespective of legislation and\nawareness regarding the accessibility gap. The hypothesis is grounded in the observation that\nvisual aspects are prioritized during web development, potentially overlooking the\nimportance of web accessibility. This is an important topic of research since nearly 20% of\nthe world's population (World Health Organization 2023), have a disability and almost\neveryone will experience disability at some point in their life (World Health Organization\nn.d.).\nUtilizing quantitative methods to conduct an empirical test this research evaluated the web\naccessibility of 49 Canadian Universities over a 15-year time period, from 2008 to 2022.\nData collection utilized online sources such as the Wayback Machine.\nThe research findings reveal that during periods of web advancements, there is an initial\nincrease in web accessibility issues, validating the negative impact of digitalization on web\naccessibility. However, over time, there is a noticeable reduction in these issues, indicating\nan overall improvement in web accessibility. One significant factor negatively impacting\nweb accessibility identified in this study is, the visual advancements brought about by\ndigitalization. The effectiveness of legislation in enhancing web accessibility was\ninvestigated, focusing on compliance deadlines. The study demonstrates that compliance\ndeadlines do not lead to increased accessibility on the web, meaning legislation fails to\neffectively improve web accessibility.\nOverall, this research highlights the immediate inaccessibility of the web resulting from\ndigitalization. These findings emphasize the ongoing need to prioritize web accessibility\namidst digital advancements. From these findings, stakeholders can work towards a more\ninclusive and accessible web environment for PwD.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".