De-and re-bordering the Alpine Space: how Cross-border Cooperation Intertwines Spatial and Institutional Patterns of Exclusion and Inclusion, Subordination and Horizontality
Bibliographic record
Abstract
During the last years, scholars have broken up dichotomies that have shaped our understanding of cross-border cooperation. In geography, the confrontation between a territorial and a relational reading of space has given way to approaches that stress their dialogue. In political science, a struggle between a focus on government and governance has shifted towards a recognition of their coexistence. In this sense, cross-border networks no longer appear as antipodes to territorial borders, scalar relationships, sectoral differentiation and political hierarchies. Rather, they constitute and condition each other. While both geography and political science stress how connections mingle with patterns of exclusion and subordination, scholars rarely bring spatial and institutional accounts together. This paper aims at bridging the gap between spatial and institutional approaches of cross-border cooperation. With regard to theory, it embeds similarities in their ontological focus on structures and strategies in a strategic-relational approach. Empirically, the paper examines the EU macro-regional strategy for the Alpine space. The conclusions imply that the macro-regional strategy embodies a dynamic balance of spatial and institutional boundlessness and boundaries.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".