Cultures of Border Control: Schengen and the Evolution of Europe's Frontiers
Bibliographic record
Abstract
The dissertation examines one of the most remarkable and controversial developments in the recent history of European integration, namely the institutionalization of a regional policy regime to manage the continent’s frontiers. By adopting this regime (known in policy circles as ‘Schengen’), European governments have in fact relinquished part of their sovereign authority over the politically sensitive issue of border control, thereby challenging what for a long time was the dominant national approach to policy-making in this domain. In order to account for the regime’s emergence and success, a constructivist analytical framework centred on the notion of ‘cultures of border control’ is advanced. From this perspective, the adoption of a regional approach to govern Europe’s frontiers is the result of the evolution of a nationalist (‘Westphalian’) culture—or set of background assumptions and related practices about borders shared by a given policy community—into a post-nationalist one (‘Schengen’). The cultural evolutionary argument elaborated in the dissertation captures the unique political dynamics that have characterized border control in Europe in the last two decades and offers a more nuanced account of recent developments than those available in the existing European Studies literature. It can also shed light on current trends defining European politics beyond border control (e.g., Europe’s policy towards its neighbours) and on other attempts to regionalize border control outside Europe (e.g., the proposal for a North American security perimeter).
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".