Politiken sozialer Mischung und die Produktivität von Rassismus im „gefährlichen Viertel“
Bibliographic record
Abstract
Abstract. In Germany, strategies of “social mixing” aiming at the development of “disadvantaged” neighborhoods have been widely established in urban politics. Such strategies are oriented towards attracting middle class residents and therefore are discussed as drivers of displacement and exclusion of “the disadvantaged”. In our paper, we analyze such urban transformation processes focusing on the productivity of racial classifications. We present results of a research project in which we examine the appropriation of urban resources (such as housing, neighborhood infrastructures, and public spaces) by residents in such quarters. Using an interactivist approach “from below” and qualitative research methods, our case study on the former “declining”, currently “stagnating” quarter Essen-Altendorf shows the productivity of racial classifications in the residents' negotiations on “rules” in the closer neighborhood or on the use of public spaces. Yet, racializing articulations correspond with the policies of social mixing as well as with the observed selective policing of a “dangerous quarter”.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".