Balancing the movement and place functions of streets: an integrated framework for research
Bibliographic record
Abstract
Street design remains largely rooted in traditional transportation principles, prioritizing movement speed and efficiency (Handy, 2023), even when planning for non-motorized modes (Verkade & Te Brömmelstroet, 2022). Yet, mobility is only one of many functions of streets. They are spaces of social, economic, and cultural interaction and these functions have been undervalued in modern planning considerations (Dehghanmongabadi & Hoşkara, 2022). These social functions, grounded in the idea of streets as public places, are often in tension with the mobility uses. An integrated, interdisciplinary framework can guide research in understanding the relationships between these movement and place functions and help balance the increasing demands on streets to serve multiple constituencies. To this end, this paper synthesizes the literature from the various disciplines that have used the street as a locus of study. It has the following aims: a) identifying the key phenomena, concepts, and relationships that define and influence the various mobility and place functions; b) developing an integrated approach for examining the inter-relationships between these functions; and c) providing insights into how various qualitative and quantitative techniques from these disciplines, aided by advances in technology, can serve a research agenda on the public right-of-way. A particular focus will be placed on emerging technologies, such as those used to analyze smartphone data, video recordings, street view images, and virtual reality. The contributions here promote a holistic and interdisciplinary understanding of streets as public spaces and thoroughfares and motivate a transformation in scholarship and practice.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".