Identifying cross-border functional areas: conceptual background and empirical findings from Polish borderlands
Bibliographic record
Abstract
Preparations for the EU’s post-2020 Multiannual Financial Framework have brought increased interest to the functional approach as a major paradigm of the EU policies towards cross-border areas. This approach aims to focus cross-border programmes on territories where there is a high degree of cross-border interaction. Cross-border functional areas (CBFAs) can be a potential instrument for this, fostering further reduction of cross-border barriers and enhancing flows of people, goods, materials and knowledge. However, certain aspects of this notion are rather vague. This includes both the way how to turn the rather discursive concept of the CBFA into more material-institutional practices, and how CBFAs can be identified in practice to successfully implement the EU’s cohesion policy. This paper debates the concept of the CBFA and proposes understanding CBFAs as spatially specific territorial complexes, located on two (or more) sides of a state border(s) that are not defined by administrative borders, but by cross-border functional linkages, a system of cooperative relationships and the existence of governance mechanisms. The paper proposes a novel approach for CBFA’s identification based on a four-level model, taking into account the selected criteria. The proposed framework enabled to identify CBFAs and potential CBFAs at the borders of Poland.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".