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Record W4392594054 · doi:10.1080/14442213.2024.2320926

Disposable People as Infrastructure? The Livelihood Trials and Tactics of Three-Wheeler Delivery Drivers on Hanoi’s Streets, Vietnam

2024· article· en· W4392594054 on OpenAlexfundno aff
Sarah Turner

Bibliographic record

VenueThe Asia Pacific Journal of Anthropology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLivelihoodVietnameseNegotiationParatransitCapital (architecture)Capital cityMobilitiesTransportation infrastructureEconomic JusticeBusinessEthnographyPolitical scienceSociologyEngineeringGeographyTransport engineeringLawMarketingEconomic geography

Abstract

fetched live from OpenAlex

The Vietnamese state is envisioning Hanoi as a prosperous, ‘civilised’ capital city with fast, ‘modern’ mobilities and their corresponding infrastructures, including expressways and an elevated railway. Concurrently, slower informal paratransit are increasingly discouraged and marginalised, threatening the livelihoods of hundreds of three-wheeler motorbike delivery drivers. Despite official registration as disabled war veterans, ‘real’ three-wheeler drivers find themselves in an ever more conscribed environment, while other drivers attempting to maintain livelihoods in this way are deemed ‘fake’ by officials and further ostracised. Drawing on conceptual debates regarding people as infrastructure and mobility (in)justice, and ethnographic fieldwork with three-wheeler drivers, I detail how drivers (both ‘real’ and ‘fake’) must negotiate inconsistent policies, a growing discourse that they are obsolete and hence disposable, and new infrastructures incompatible with their livelihoods. Combined, these elements create specific mobility experiences and frictions to which drivers react with subtle and inventive tactics to maintain their rights to the city’s 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 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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.306
Teacher spread0.289 · 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.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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