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Record W3085580078 · doi:10.1177/0042098020949035

Gentrification or …? Injustice in large-scale residential projects in Hanoi

2020· article· en· W3085580078 on OpenAlexafffund
Cuz Potter, Danielle Labbé

Bibliographic record

VenueUrban Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaKorea University
KeywordsGentrificationInjusticeSociologyContext (archaeology)PoliticsScale (ratio)SlumSocial injusticePolitical economyEconomic growthPolitical scienceEconomicsGeographyLaw

Abstract

fetched live from OpenAlex

Large-scale residential developments on expropriated lands in periurban Hanoi resemble forms of gentrification seen elsewhere. But is it gentrification? Current debate over the definition of gentrification has focused on whether the term has become too broad to be useful in different institutional and spatio-temporal contexts. While some push for a generalisable definition based in capitalist development, others argue that the term harbours Western assumptions that fail to usefully explain unique local circumstances. The paper first identifies one such conceptual assumption that must be made explicit since it provides the term’s politicising thrust: displacement generates an experience of social injustice. Then, drawing on surveys and interviews with residents as well as interviews with real estate agents, government officials and academics conducted in Hanoi between 2013 and 2017, the paper evaluates five types of displacement on the city’s outskirts. Because displacement only occurs in marginal cases and generates limited feelings of social injustice, the term ‘gentrification’ is of little use. Instead, the paper suggests that in a context of rapid urbanisation and relatively inclusive economic growth such as that of Hanoi the terms ‘livelihood dispossession’ and ‘value grabbing’ may better capture the experience of social injustice and are therefore more likely to generate political traction.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.090
GPT teacher head0.349
Teacher spread0.259 · 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 designObservational
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

Citations16
Published2020
Admission routes2
Has abstractyes

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