Analyzing Mobility Gaps Between People with and without Disabilities using Oaxaca-Blinder Decomposition Method
Bibliographic record
Abstract
ABSTRACT: Some population groups have lower propensity to be mobile than others. The reasons for this divergence in tendencies are difficult to assess namely since some of these groups have vulnerability features and are typically less observed and analyzed in regular surveys. The aim of this research is to compare the mobility of people with and without disabilities. Data from the 2019 Origin-Destination survey for Montreal City (Canada) allows to calculate four mobility indicators. 54% of the people with disabilities are immobile during a typical weekday compared to 17% for the other group. To understand the source of the mobility gap, two estimations with Oaxaca-Blinder decomposition method are used. It has been found that a portion of this gap is explained by different characteristics of the two groups (proportion of older adults, workers, and people with driver license). The gap is also explained by disparities between people with similar characteristics. Women and older adults with disabilities are less mobile than women and older adults without disabilities. The analysis of people with and without disabilities satisfaction suggests that the perception of PT is a factor associated with the mobility gap between the two groups, but further research will be necessary.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".