Development and implementation of a framework for estimating the economic benefits of an accessible and inclusive society
Bibliographic record
Abstract
Purpose To develop a framework for estimating the economic benefits of an accessible and inclusive society and implement it for the Canadian context. The framework measures the gap between the current situation in terms of accessibility and inclusiveness, and a counterfactual scenario of a fully accessible and inclusive society. Design/methodology/approach The method consists of three steps. First, the conceptual framework was developed based on a literature review and expert knowledge. Second, the magnitudes for each domain of the framework was estimated for the reference year 2017 using data from various sources. Third, several sensitivity analyses were run using different assumptions and scenarios. Findings It was estimated that moving to a fully accessible and inclusive society would create a value of $337.7bn (with a range of $252.8–$422.7bn) for Canadian society in the reference year of 2017. This is a sizeable proportion of gross domestic product (17.6%, with a range of 13.1–22.0%) and is likely a conservative estimate of the potential benefits. Originality/value Understanding the magnitude of the economic benefits of an accessible and inclusive society can be extremely useful for governments, disability advocates and industry leaders as it provides invaluable information on the benefits of efforts, such as legislation, policies, programs and practices, to improve accessibility and inclusion of persons with disabilities. Furthermore, the total economic benefits and the benefits per person with a disability can serve as inputs in economic evaluations and impact assessments.
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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 | 0.046 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".