The ambiguity of diversity: Management of ethnic and class transitions in a gentrifying local shopping street
Bibliographic record
Abstract
As a malleable concept with a relatively positive resonance, ‘diversity’ proves to be a useful tool to legitimise a range of policy strategies, goals and outcomes. In the Netherlands, the concept has gained a central role in the implementation of social mixing policies targeting so-called problematic neighbourhoods by introducing a better ‘mixed’ or ‘balanced’ population. The discursive celebration of such a mixed neighbourhood, however, often carefully evades the question: ‘A mix of what?’ Closer inspection of policy interventions reveals that the different meanings of diversity are employed to claim urban space for some groups, while excluding others. This is illustrated by a range of micro-management strategies in a shopping street in Amsterdam, Javastraat. Framed as promoting diversity, they form a symbolically loaded strategy to covertly manage ethnic and class transition by targeting the retail landscape. This article explores the (discursive) remaking of the shopping street and the consequences thereof for shopkeepers and local residents.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".