Rhythmanalysis of pedestrian streets in Hanoi: A spatial–temporal reading of public spaces
Bibliographic record
Abstract
Streetspace reallocation has been drawing considerable attention from city governments and practitioners, especially since the COVID-19 pandemic. Over the last ten years in Vietnam, pedestrianization has rapidly been adopted by many cities across the country. Despite this, there is still a gap in our understanding of how pedestrianization is conceived and used in Vietnam and in other parts of the Global South, where high population densities and informal economic activities shape urban public spaces. Our research explored how pedestrian streets are imagined, used, and negotiated by different user groups (planners, locals, informal vendors, and visitors) in downtown Hanoi. Drawing on rhythmanalysis (Lefebvre, 1992), our conceptual framework included analyses of the street’s usage as well as socio-political aspects of rhythms. We conducted systemic observations of the pedestrian street in the spring of 2022 and 70 in-depth interviews in the summer of 2022. This research enriches the conceptualization of rhythms by introducing the dominant-adapting-dominated rhythms triad, which uncovers a network of power dynamics that limit informal-sector street vendors’ access to public spaces. By characterizing street sectors based on magnitude and types of rhythms, we demonstrate the methodological significance of rythmanalysis. Our findings offer valuable insights for policymakers and designers seeking to create more inclusive pedestrian streets.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".