Redes de políticas en el marco de la Agenda Urbana para la UE. La influencia de las coaliciones temáticas en la politización de la toma de decisiones a escala europea
Bibliographic record
Abstract
This article explores the role of multilevel thematic partnerships method in building a specific policy, namely, the Urban Agenda for the European Union (EU) adopted in 2016. This method, designed as a pilot experiment, formalizes cooperation among different levels of government as well as public and private actors. The purpose of this paper is to examine the implementation of the multilevel governance approach and to determine the extent to which the thematic partnerships method contributes to the politicization of a decision-making process at the European level. For this purpose, the methodological design is based on network analysis applied to four case studies, namely, the following four thematic partnerships: Inclusion of Migrants and Refugees, Urban Poverty, Climate Action and Security in Public Spaces. Firstly, the results show a higher mobilization of stakeholders representing local interests; secondly, it is also shown that the monetization of the problem determines the nature of stakeholders (stakeholders representing social or economic interests) that join the policy networks. In sum, this article makes an empirical contribution to multilevel governance and politicization studies on the one hand, and to urban and European studies on the other.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".