Experimenting on racialized neighbourhoods: Internationale Bauausstellung Hamburg and the urban laboratory in Hamburg–Wilhelmsburg
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".