A Measure of Competitive Access to Destinations for Comparing Across Multiple Study Regions
Why this work is in the frame
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Bibliographic record
Abstract
Accessibility is now a common way to measure the benefits provided by transportation–land use systems. Despite its widespread use, few measurement options allow for the comparison of accessibility across multiple urban systems, and most do not adequately control for market competition between demand‐side actors and supply‐side facilities in localized markets. In this article, we develop a measure of competitive access to destinations that can be used to accurately compare accessibility between regions. This measure stems from spatial interaction modeling and accounts for competition at both the supply and demand sides of analysis, regional differences in transportation networks and travel behavior, and any imbalance between the size of the population and the number of opportunities. We use this method to compute access to employment for Canada's eight largest cities to comparatively examine inequalities in accessibility, both within and between cities, and by travel mode.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it