The residential patterns of Swiss urban elites: continuity and change across elite categories (1890–2000)
Bibliographic record
Abstract
ABSTRACT Numerous studies have focused on wealth elites’ housing, including their spatial and social exclusiveness. The insertion of the power elite in urban space has, however, largely been left unexplored. By combining positional and residential information on over 7,400 urban elites, we study how academic, economic, and political elites’ residential patterns have evolved from 1890 to 2000 in the three largest Swiss cities (Basel, Geneva, Zurich). First, we uncover a long-term dynamic of suburbanization, which however does not result in even spatial dispersion: while gradually abandoning center cities, elites do not randomly disperse in the surrounding municipalities. Rather, they tend to settle in very specific areas. Second, we find that spatial differentiation of urban elites’ residences varies across elite categories: economic elites tend to geographically segregate from both academic and political elites over the course of the twentieth century and settle in more privileged areas. At the same time, academic and left political elites, while historically living in distinct neighborhoods, tend to converge at the end of the century, echoing new similarities in their profile. This highlights the importance of studying the urban power elites’ residential patterns in a long-term perspective.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".