Probabilistic Causal Data Modeling of Barriers to Accessibility for Persons with Disabilities in Canada
Bibliographic record
Abstract
This thesis addresses the pressing social issue of accessibility for persons with disabilities by creating AI models using the national survey data, with a methodological two step process. The contributions of this thesis include (a) a causal reasoning framework aiming to provide an understanding of the prevalence of barriers and challenges faced by persons with disabilities when accessing federal services and facilities; and (b) network-based approach that utilizes empirical data to provide a holistic assessment of the causality among demographic features (e.g. age, gender, type of disability) and accessibility. The statistical method utilizes Structural Equation Modeling supported by Exploratory Factor Analysis. For causal probabilistic modeling, Bayesian Networks are employed as a straightforward and compact way to interpret knowledge representation. This causal reasoning approach analyzes the nature and frequency of encountering barriers based on data to understand the risk factors contributing to pressing accessibility issues. Furthermore, to evaluate the network performance and overcome data limitation, synthetic data generation techniques are applied to create and validate artificial data built on real-world knowledge. The proposed framework aims to provide reasoning to understand the prevalence of physical, social, communication or technological barriers encountered by persons with disabilities in their daily lives. This thesis contributes to identifying areas for prioritization in facilitating accessibility regulation and practices to build an inclusive society.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".