Co-variates of Multimodal Accessibility in Canadian Cities
Bibliographic record
Abstract
Accessibility has become one of the predominant ways of understanding the relationship between transportation and land use in urban areas. Traditional measures of accessibility understand it unimodally or comparatively, without consideration of the dynamics of a multimodal transportation system. Multimodal, or mode share weighted accessibility (MWA) measures, take into account observed mode shares of the underlying geographic units and apply them to the accessibility to employment provided by that mode share. The individual MWA values are then added to give a singular MWA value. In this research MWA models are created for over 20 Canadian census metropolitan areas. They’re presented at regional and census tract levels, where the latter are then used in regression models to understand correlations that exist between MWA and socioeconomic and demographic factors. Inferential statistics are used to estimate differences in means of the socioeconomic and demographic variables of the top and bottom quintiles of MWA in every region. Many of the socioeconomic factors were found to be significantly corelated with MWA, with higher MWA values being associated with higher median household incomes, lower proportions of renters, and typically lower population density and lower proportions of visible minorities and immigrants. This is the first study to use multimodal accessibility models to understand the relationships between accessibility and socioeconomic factors across large- and medium-sized metropolitan regions in Canada.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".