At Home in Hamburg-Wilhelmsburg: Racialized Long-Time Residents' Perspectives on Urban Development and Social Mix Planning
Bibliographic record
Abstract
After decades of stigmatization, the historically working class and immigrant neighbourhood of Wilhelmsburg, in Hamburg, Germany, is experiencing a flurry of planning and development attention from the city-state. The city evaluates neighbourhood change mainly by tracking demographics, and in particular the attraction of the white, German middle class to the island. Little is known about the qualitative experiences of long-time residents, however, and even less about the experiences of racialized people. This is consistent with the pattern of inattention to racialization in German urban research, which has led Black scholars and scholars of colour to call for more scholarship that takes seriously the role of structural and systemic racism in the production of urban space. A public ethnography informed by an intersectional, anti-racist methodology, this study responds to this call by exploring the perspectives of racialized long-time residents of Wilhelmsburg on recent developments on the island and investigating how racialized people figure in local planning. Through ethnographic interviews with nineteen residents and eight planners and politicians, as well as archival research, photography and participant sensing, the study illuminates a complex picture of development in Wilhelmsburg past and present. This dissertation draws on and extends theories of racial capitalism, the legacies of colonialism in Hamburg, racism and migration in Germany, and social mix planning and gentrification. It finds that Wilhelmsburg has a long history of devaluation as a space associated with waste and migrant labour. In contrast, the interviewed racialized residents value the island differently, as a Heimat: a place of warmth and belonging in a context that otherwise excludes them. It further finds that the citys recent social mix policies and projects in Wilhelmsburg rely upon treatment of racialized people as more displaceable under the law, and that the citys planning strategies represent a threat to racialized belonging in the neighbourhood as a result. The interviewed residents challenge the dominant planning narrative with their assessments of the effects of advancing gentrification on the neighbourhoods most vulnerable, and contest the meaning of mix with interpretations that value the islands longstanding diversity and support their hopes for a more convivial future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".