The citizen in city-regions:Patterns and variations
Bibliographic record
Abstract
With the growth of city-regions, the local citizenship may increasingly become city-regional rather than municipal. This article provides a meta-analysis of the current state of knowledge with regard to general patterns of city-regionalism; that is, citizens’ orientations toward political matters in the city-region, beyond one’s own municipality. The theoretical framework draws from theories of participation, citizen integration, and democratic scale. The analysis is based on 12 publications, making use of the 8 surveys from 7 countries that have been carried out in the Western world since 2000. The analysis provide support for the theoretical assumptions, but because the data are not directly comparable, conclusions are formulated as hypotheses: It is suggested that city-regionalism is stronger in larger and fragmented city-regions. Further, those living in the suburban municipalities hold stronger intermunicipal interests, attitudes, and identities but those in the core city are more in favor of redistributive regional reform. Finally, cityregionalism is stronger among those who are mobile in the city-region, are better educated, have a general interest in politics, and belong to the political left. The findings have implications for how democratic participation and governance may be organized in city-regions. Further and internationally coordinated studies are required.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".