Measuring and moving on the street: A scoping review of street space allocation studies
Bibliographic record
Abstract
A field of research is emerging that examines the allocation of street space to different transportation infrastructures, backgrounded by the increasingly recognized need to redesign city streets away from the dominance that cars have held over them for the past century. In this scoping review, we systematically search the literature to identify 12 peer-reviewed journal articles that use empirical methods to study street space allocation to transportation modes, synthesizing and reflecting on the studies’ methodologies, results, and identified policy implications and future research areas. From this synthesis, key themes emerge around how the studies frame their work in the transportation justice literature and toward conceptualizing an equitable streetscape, the differences in the methodologies employed and promising avenues to improve their methods, and the difficulties in comparing results across studies. Stemming from the reviewed studies, this review offers several directions for future research to encourage the development of street space allocation research, a field well-positioned to contribute to research and policy around critiquing and improving city streets and urban livability.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".