‘At least it’s not a ghetto anymore’: Experiencing gentrification and ‘false choice urbanism’ in Rotterdam’s Afrikaanderwijk
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.037 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".