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Accessibility Monitoring for People with Disabilities

2021· book-chapter· en· W4200307704 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIGI Global eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsYork University
Fundersnot available
KeywordsLegislationInternet privacyWeb accessibilityMobile appsAndroid (operating system)Embodied cognitionDisabled peopleAndroid appUniversal designAssistive technologyComputer scienceBusinessComputer securityPublic relationsWorld Wide WebPolitical scienceHuman–computer interactionPsychologyThe InternetLaw

Abstract

fetched live from OpenAlex

The Accessibility for Ontarians with Disabilities Act (AODA) is a law mandating that organizations in Ontario must comply to accessibility standards for people with disabilities. However, there is no tool to report accessibility complaints and track them. To that effect, mobile applications can be effective to make report and monitor accessibility issues as they arise in private as well as public spaces (e.g. building, sidewalks). An App would provide users with an opportunity beyond the mapping of compliance, it can provide data that addresses the gaps across legislation and embodied experiences. The objective of this paper is to share a novel method associated with the development accessibility monitoring Android App prototype called “ACCESS-ABILITY.” ACCESS-ABILITY is a first-of-its-kind app in the domain of disability informatics, it facilitates the formation of a collaborative virtual community that can be used by people with disabilities, advocacy groups, organizations and official bodies.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.324
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it