"Slipping through the cracks": A Duoethnography of Web Accessibility
Bibliographic record
Abstract
The web is inaccessible. According to a 2024 review of 1 million websites, 95.9% of home pages online had one or more accessibility failures—and many had over fifty automatically-detected issues [2]. Despite years of work analyzing accessibility failures and paths towards improvement, comparatively little work at ASSETS focuses on the labor of doing web accessibility. Drawing from a duoethnography of working on accessibility, this position paper explores the practical work of accessibility in academic and professional environments and makes concrete recommendations for web accessibility practitioners. We argue that accessibility is not primarily an issue of awareness or an overreliance on compliance: we show that accessibility is much better understood as a complex resource management problem that requires multifaceted and ongoing effort to maintain.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.010 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".