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Record W7055300169

The citizen in city-regions:Patterns and variations

2018· article· en· W7055300169 on OpenAlexaff

Bibliographic record

VenueTilburg University Research Portal · 2018
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsCitizenshipPoliticsDemocracyState (computer science)Core (optical fiber)Corporate governance
DOInot available

Abstract

fetched live from OpenAlex

With the growth of city-regions, the local citizenship may increasingly<br/>become city-regional rather than municipal. This article provides a meta-analysis<br/>of the current state of knowledge with regard to general patterns<br/>of city-regionalism; that is, citizens’ orientations toward political matters in<br/>the city-region, beyond one’s own municipality. The theoretical framework<br/>draws from theories of participation, citizen integration, and democratic<br/>scale. The analysis is based on 12 publications, making use of the 8 surveys<br/>from 7 countries that have been carried out in the Western world since<br/>2000. The analysis provide support for the theoretical assumptions, but<br/>because the data are not directly comparable, conclusions are formulated<br/>as hypotheses: It is suggested that city-regionalism is stronger in larger and<br/>fragmented city-regions. Further, those living in the suburban municipalities<br/>hold stronger intermunicipal interests, attitudes, and identities but those in<br/>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<br/>better educated, have a general interest in politics, and belong to the<br/>political left. The findings have implications for how democratic participation<br/>and governance may be organized in city-regions. Further and internationally<br/>coordinated studies are required.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.282
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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