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Record W3008827281 · doi:10.1177/0263775820903328

Experimenting on racialized neighbourhoods: Internationale Bauausstellung Hamburg and the urban laboratory in Hamburg–Wilhelmsburg

2020· article· en· W3008827281 on OpenAlexafffund
Julie Chamberlain

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRacializationNeighbourhood (mathematics)SociologyGentrificationFraming (construction)Context (archaeology)Urban planningUrbanismGender studiesMedia studiesGeographyArchitectureEconomic growthRace (biology)

Abstract

fetched live from OpenAlex

The ‘urban laboratory’ concept has become a popular discourse and structure for urban projects in recent years. In this article, I ask what the concept achieves in the context of racialized urban disinvestment and stigmatization, with the Hamburg International Building Exhibition’s (Internationale Bauausstellung Hamburg, 2006–2013) work in Hamburg–Wilhelmsburg as an example. Drawing on laboratory studies and urban sociology, I sketch out the laboratory as an ‘imaginative infrastructure’ , including what it offers to the ‘investigators’ who use it. Based on the ‘situatedness’ of the urban laboratory, I argue that the racialization of the neighbourhood and its residents is important to the history of Wilhelmsburg’s planning, and to its recent laboratorization. Laboratorization of racialized people and spaces is not new, but rather has a long history in European colonialism. I conclude that though the experiment in Hamburg–Wilhelmsburg appears to be with the neighbourhood’s racialization itself, there is nothing experimental about attempting the planning myth and common sense of ‘social mix’ to which Internationale Bauausstellung Hamburg contributes. I argue that racialized Wilhelmsburgers deserve problem-solving that does not reinforce existing patterns of development and thus their stigmatization.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.269
Teacher spread0.247 · 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

Citations19
Published2020
Admission routes2
Has abstractyes

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