Procedurally reducing complexity. The practices of German EU policy coordination
Bibliographic record
Abstract
Policy coordination in federal states is inherently complex because it includes a multitude of actors at the federal and the sub-state level. If the sub-states want their interests to be included in the final decision, they need to coordinate with the federal level but also amongst themselves. Several individual interests areoverlooked easier than coordinated interests of a group of sub-states. This paper puts forward the argument that during the coordination process, the actors from both levels meet in different constellations where they focus on different aspects of coordination, especially on different actors’ interests separately. This is a strategy which enables them to procedurally reduce the complexity of the decision-making process. In order to empirically investigate this argument, first a thorough definition of coordination as process is provided and operationalized for empirical investigation. It is accentuated that coordination as a process has different dimensions which are relevant for the understanding of the coordination process. This argument is analyzedwith the example case of German EU policy. The empirical data used are original expert interviews with German civil servants responsible for EU policy coordination at the sub-state level. It will be demonstrated that the actors strategically form voluntary coordination constellations which enables them to reduce complexity during the process.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".