Advances and pitfalls in measuring transportation equity
Bibliographic record
Abstract
Transportation systems play a pivotal role in facilitating access to out-of-home activities, enabling participation in various aspects of social life. But because of budgetary and physical limitations, they cannot provide equal access everywhere; inevitably, some locations will be better served than others. This realization gives rise to two fundamental concerns in transportation equity research and practice: 1) accessibility inequality and 2) accessibility poverty. Accessibility inequalities may rise to the level of injustice when some socioeconomic groups systematically have lower access to opportunities than others. Accessibility poverty occurs when people are unable to meet their daily needs and live a dignified, fulfilling life because of a lack of access to essential services and opportunities. In this paper, we review two of the most widely used approaches for evaluating transport justice concerns related to accessibility inequality and accessibility poverty: Gini coefficients/Lorenz curves and needs-gap/transit desert approaches, respectively. We discuss how their theoretical underpinnings are inconsistent with egalitarian and sufficientarian concerns in transport justice and show how the underlying assumptions of these methods and their applications found in the transportation equity literature embody many previously unacknowledged limitations that severely limit their utility. We substantiate these concerns by analysing the equity impacts of Covid-19-related service cuts undertaken in Washington, D.C. during 2020. The paper also discusses how alternative methods for measuring transportation equity both better comport with the known impacts of such changes and are consistent with underlying moral concerns.
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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 | 0.068 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".