Governance and State Power: A Network Analysis of European Security
Bibliographic record
Abstract
Abstract A growing number of scholars argue that the development of the common security and defence policy (CSDP) should be analysed as the institutionalization of a system of security governance. Although governance approaches carry the promise of a sophisticated, empirically grounded picture of CSDP, they have been criticized for their lack of attention to power. This is because governance approaches focus on institutional rules and ideas rather than the social structure that underpins them. To refine the notion of security governance, this article analyses co‐operation patterns through social network analysis. Confirming the governance image, it maps out a complex constellation of CSDP actors that features cross‐border and cross‐level ties between different national and EU policy actors. It is also found, however, that CSDP is dominated by a handful of traditional state actors – in particular, Brussels‐based national ambassadors – who retain strategic positions vis‐à‐vis weaker supranational and non‐state actors. These actors are not giving up on state power, but reconstituting it at the supranational level.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".