INTRODUCTION—Frontiers in Motion (Frontem): Comparative Perspectives on European Borders, Cross-Border Cooperation, and Integration
Bibliographic record
Abstract
This special section, edited by the author, presents five articles developed from the Frontiers in Motion (Frontem) doctoral seminar held in Strasbourg, France, in October 2021, on “Borders in Motion: Borders, Cross-Border Cooperation and European Integration”. The event was organized within the framework of the Jean Monnet Network, “Frontières en mouvement: quels modèles pour l’Union Européenne (Frontem)?” (“Frontiers in Motion: what models for the EU?”), which aimed at fostering knowledge and practice exchanges on cross-border management models and various perceptions of borders across European (and North American) border regions. The diverse contributions illustrate the complexity of borders in Europe and that there has never been an abolition of all types and functions of borders in the European Union (EU). It therefore offers a critical reading of the “Europe without borders” model of the EU (Cooper 1989; Brunet-Jailly & Wassenberg 2020). Note: The network Frontières en mouvement: quels modèles pour l’UE? (611115-EPP-1-2019-1-FR-EPPJMO-NETWORK) is a Jean-Monnet Network supported by the EU’s Erasmus+ program for the period between 2019 and 2023 under the leadership of the author.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".