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Record W3217198953 · doi:10.4000/civilisations.5778

Youth negotiations: Navigating public space access and urban planning transformations in Hanoi, Vietnam

2020· article· en· W3217198953 on OpenAlexaff
Madeleine Hykes, Sarah Turner

Bibliographic record

VenueCivilisations · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsNegotiationPublic spaceSpace (punctuation)Urban spaceBusinessGeographyPolitical scienceEnvironmental planningArchitectural engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Since the mid-1990s, municipal authorities in the Socialist Republic of Vietnam’s capital, Hanoi, have encouraged rapid urban development and expansion with little public input or feedback. Private gated communities, high-rise apartment blocks, and vast shopping malls are proliferating alongside new transportation infrastructure. While these transformations create new opportunities for some, others face increasing marginalisation and inequality. This paper draws conceptually on debates concerning youth in public spaces, youth and post-socialist cities, and everyday politics and on fieldwork in Hanoi to analyse the impacts that urban morphological changes are having on Hanoi’s heterogeneous youth cohort, and their responses. We find youth centrally concerned by rising pollution and diminishing green spaces and increasingly searching for new ‘pseudo-public’ spaces for leisure activities. Many are also suspicious of investment sources for new infrastructure and frustrated by a lack of access to accurate information. Yet, despite limited opportunities to voice concerns regarding the state’s bold development plans for the country’s capital, youth do not remain passive. While differentiating by socioeconomic class, we suggest that youth navigate current transformations and policies through a range of resourceful tactics. They are also vocal and innovative regarding future urban scenarios that they wish to see implemented.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.653
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.097
GPT teacher head0.333
Teacher spread0.237 · 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 teacher head, not a consensus.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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