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Record W2803001517 · doi:10.1177/0042098018761853

‘At least it’s not a ghetto anymore’: Experiencing gentrification and ‘false choice urbanism’ in Rotterdam’s Afrikaanderwijk

2018· article· en· W2803001517 on OpenAlexaff
Brian Doucet, Daphne Koenders

Bibliographic record

VenueUrban Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGentrificationNeighbourhood (mathematics)SociologyUrbanismContext (archaeology)Economic geographyGender studiesEconomic growthPolitical economyGeographyEconomicsArchitecture

Abstract

fetched live from OpenAlex

Gentrification has become a central pillar of urban policy in cities around the world. Proponents often frame it as a necessity and the sole alternative to neighbourhood decline. Critics call this a ‘false choice’ as it ignores other possibilities for improvement without gentrification. But how do working-class residents who live through the process of gentrification view the impact it has on their neighbourhood? Do they see it in such a stark binary way? This article addresses these questions by using qualitative interviews with long-term residents of the Afrikaanderwijk, a multicultural neighbourhood in Rotterdam where municipally-led gentrification is taking place. In contrast to much of the Anglo-Saxon literature on experiencing gentrification, our respondents had far more mixed, complex and ambivalent perspectives on the process. To some extent, this was due to the neighbourhood’s recent history as a stigmatised ‘ghetto’ and the expectation that the arrival of white, ethnically Dutch middle-class people would help to improve the neighbourhood, which was ranked worst in the country in 2000. We also stress the role of local context, such as the early phase of gentrification and the comparatively strong social housing sector and tenant protection laws in the Netherlands, in contributing towards a more nuanced experience of gentrification.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.082
GPT teacher head0.350
Teacher spread0.268 · 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 designNot applicable
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

Citations30
Published2018
Admission routes1
Has abstractyes

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