Political rescaling and metropolitan governance in cross-border regions: comparing the cross-border metropolitan areas of Lille and Luxembourg
Bibliographic record
Abstract
In the last 20 years, the number of cross-border cooperation structures in Europe has exploded owing to political and financial support by the European Union aimed at encouraging cohesion and developing peripheral regions. These policies are part of processes of de-bordering and political rescaling that have profoundly affected cross-border areas by creating new institutional territories and political structures. The purpose of this paper is to study the institutional history of cross-border metropolitan governance in Europe through the comparison of two of the most advanced cross-border metropolitan regions: Lille and Luxembourg. This paper asks how these cross-border structures have developed and changed. What can their patterns of institutional evolution contribute to understanding governance in other cross-border regions? Are these new spaces evidence of political rescaling? This paper presents and redefines cross-border governance as a cyclical and a long-term process and also explores the challenges that these partnerships face in becoming functionally effective and autonomous policy actors. Ultimately, we find that there is no replicable ideal of cross-border governance and that even long-standing partnerships are in a period of exploration and reinvention. Establishing a competitive and coherent cross-border metropolitan region is ambitious and complex, and it necessitates the coordination of policies at multiple scales and across institutionally diverse territories. This project requires the modification and/or construction of new institutional and legal frameworks. This reorientation of political attention has resulted in a reconceptualization of political space but not the empowerment of new political actors, indicating that the process of rescaling may be a work in progress.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".