Developing a taxonomy of the built environment for disability studies. Methodological insights
Bibliographic record
Abstract
For a city to be inclusive, its physical environment needs to be identified, characterized and assessed prior to transformations and improvements. Such identification is the first step to impact on politics for citizens with impairments and functional limitations since environmental obstacles limits their social participation. The objective of this research was to develop a comprehensive and applicable information set for the description of the physical environment in support of the implementation of the United Nations Convention on the rights of persons with disabilities in Quebec City’s context. We developed a taxonomy based on the Human Development Model – Disability Creation Process (HDM-DCP). We reviewed documents containing nomenclatures with respect to the specific case of Quebec City’s physical environment. We organized the information under the original taxa of the HDM-DCP, this was carried out via an iterative process where elements of similar type were organized into a common level of one hierarchical branch under general categories. When categories linking objects to broader subcategories were not already identified, we expanded the structure by creating new sub-categories or hybrids. Applicability of the developed taxonomy was tested through field analyses and testing (photos of street sections) to determine whether it included all objects and infrastructures observed in the city. The resulting taxonomy was found to be useful in identifying/mapping elements of the physical environment. Both at the individual and collective level, it allows the identification of the elements that play a role in mobility, resulting in enhanced social participation or disabling situations for people with disabilities.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".