MétaCan
Menu
Back to cohort
Record W4408568717 · doi:10.1080/00330124.2025.2468680

Behavioral Mapping and Rhythmanalysis: Spatial and Temporal Patterns of Pedestrian Streets in Hanoi

2025· article· en· W4408568717 on OpenAlex
Huu Lieu Dang, Thi-Thanh-Hiên Pham, Julie‐Anne Boudreau

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Professional Geographer · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Crisis of the 21st Century
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPedestrianGeographyCartographyTransport engineeringEngineeringArchaeology

Abstract

fetched live from OpenAlex

Research on streets and public spaces in general has predominantly relied on systematic observation and behavioral mapping to investigate human behavior and its spatial patterns. This approach, however, often lacks the depth needed, especially in densely populated urban areas of Global South cities, to uncover the intricate social processes and politics that shape people’s behavior and patterns. In response to this limitation, our study takes a multifaceted approach, combining behavioral mapping (through systematic observation data) with rhythmanalysis (using general observation data and in-depth interviews) to study pedestrian streets with a case study in Hanoi, Vietnam. This research contributes to urban geography and urban studies methodology by analyzing the limitations and strengths of behavioral mapping and rhythmanalysis. We call for combining these methods in a way that allows them to complement each other. Such a combination should provide a nuanced and comprehensive understanding of the temporal and social forces that shape everyday life in public spaces.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.254
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it