The Spatial Articulation of Urban Political Cleavages
Bibliographic record
Abstract
Synthesizing and extending multiple literatures, this article develops a new approach for exploring the spatial articulation of urban political cleavages. We pursue three questions: (1) To what extent does electoral conflict materialize between rather than within neighborhoods? (2) How salient are group, place, and location in defining urban cleavages? (3) How do these sources inflect one another? To answer these questions, the article analyzes a novel longitudinal database of neighborhood-scale mayoral voting in Chicago, Toronto, and London. We find strong evidence of spatially articulated cleavages: in each city, voting patterns are equally or more geographically concentrated than the non-White population, income, and poverty. While group-based interests define Chicago’s cleavage structure, place and location are paramount in Toronto and London. We conclude by proposing a research agenda for investigating the spatiality of urban politics and advancing a preliminary typology of urban political cleavages and the conditions under which they may arise.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".