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Record W2510086352 · doi:10.5509/2016893591

Youth-Driven Tactics of Public Space Appropriation in Hanoi: The Case of Skateboarding and Parkour

2016· article· en· W2510086352 on OpenAlexaffvenue
Stephanie Geertman, Danielle Labbé, Julie‐Anne Boudreau, Olivier Jacques

Bibliographic record

VenuePacific Affairs · 2016
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversité de MontréalMcGill UniversityInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsAppropriationSpace (punctuation)SociologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Starting in the 2000s, there has been a rise in youth-led appropriation of public spaces in Hanoi, Vietnam. Through case studies of skateboarders and traceurs (practitioners of parkour) in two of the city’s formal public spaces, we explore and analyze the tactics deployed by these young urbanites to claim a part of the characteristically overcrowded and socio-politically restrictive public spaces of the Vietnamese capital. These case studies show that, by seeking to access public spaces for their new activities, skaters and traceurs have had to confront multiple sets of rules, imposed by not only the state, but also corporate actors and resident-driven surveillance. We find that skateboarders and traceurs deal with these forms of control largely through small-scale, non-ideological, and non-confrontational tactics. As a result, these youth practices have become normalized in Hanoi’s public spaces. These findings broaden the discourses on everyday urbanism and social-political transformations in post-socialist urban contexts, and shed light on the ways in which contemporary youths engage with the city.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.013
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.271
Teacher spread0.238 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2016
Admission routes2
Has abstractyes

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