Pedestrian Behavior Pedestrian Behavior and Perception in Urban Walking Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Planning pedestrian environments requires assumptions about how pedestrians will respond to characteristics of the environment as they formulate and enact their walking itineraries. As a consequence, most research interest in public environments focuses on behavior in relation to those characteristics. For example, there is a substantial body of descriptive and typological studies of pedestrian environments. Metric, geometric, and topological models have proved useful in characterizing density and direction of movement. The need to understand the mechanism of choice has prompted microscale and laboratory-based research on exploratory spatial behavior within walking districts. Studies of behavior in relation to comfort, the way in which images of places impinge on choices, and how dynamic and serial experience of the city affects individual itineraries have all developed as specialized fields of understanding. In general, studies of pedestrian environment dynamics have both diversified and multiplied as its systems and methodologies are adapted for planning other environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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