Governing Global Displacement in Austerity Urbanism: The Case of Berlin's Refugee Housing Crisis
Bibliographic record
Abstract
ABSTRACT A surge of forcibly displaced migrants into Europe in 2015 culminated in what has been referred to as the ‘refugee crisis’. As the continent's top destination country, Germany has been widely praised for its welcoming culture while heavily criticized for failures of integration. This is particularly true of Berlin — a city that has absorbed the highest number of war refugees in Europe, many of whom remain without stable accommodation. Despite its significance, the scholarship has largely neglected the housing question within the European refugee crisis. The aim of this article is to cast a critical light on the complexities and contradictions glossed over by the refugee crisis trope. Drawing on the Berlin case, the author argues that resettlement initiatives need to be understood against the backdrop of austerity urbanism, particularly its insistence that markets can meet housing demand. By focusing on three types of shelter provisioning for refugees, the article reveals the multifaceted and nuanced ways in which the Berlin government, refugees and grassroots organizations contest, produce and navigate the moving frames of austerity urbanism in search of stable housing.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".