A briefing paper on the state of the art of accessibility research focusing on disabled people, including research on attitudinal accessibility and attitudinal barriers
Bibliographic record
Abstract
Disabled people face many accessibility problems in their lived reality as evident by the United Nations Convention on the Rights of Persons with Disabilities (CRPD). Many societal developments and discussions can influence accessibility and be influenced by accessibility. The objective of this briefing paper for the members of the Social Sciences and Humanities Research Council of Canada (SSHRC) Insight grant, 435-2024-0750; Computational Urban Accessibility: Understanding, Mapping, and Scoring Barriers in the Built Environment was to better understand the academic coverage of accessibility-related to disabled people, with a particular focus on attitudinal accessibility and attitudinal barriers. We performed a scoping review of abstracts from Scopus, Web of Science, and the 70 databases accessible through EBSCO-HOST using manifest coding and thematic content analysis approaches to fulfill the objective. Our analysis generated various insights: for instance, of the 526,556 abstracts that contained the term “accessibility” only 6.9% also included the disability terms we used. For some other accessibility related terms, the % was even lower. The term “attitudinal accessibility” was only found in 48 full texts, of which 16 had content related to disabled people. Healthcare was the main topic mentioned under attitudinal barriers. Words and phrases such as health, healthcare, “public health”, rehabilitation and “health services” were mentioned substantially, others such as intersectionality, health promotion, “social determinants of health”, health equity, science and technology governance terms and others reflecting various social discussions were rarely mentioned. International normative legal documents were rarely mentioned or not at all. Our findings highlight significant opportunities to engage more deeply with multifaceted accessibility issues affecting disabled people, including the use of occupational rights-based, ability judgment-based, intersectionality-based and spatial rights-based concepts as analytical tools. Such efforts are essential for advancing social inclusion, health and wellbeing and equity of disabled people.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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".