Unraveling the Central State, but How? Types of Multi-level Governance
Bibliographic record
Abstract
The reallocation of authority upward, downward, and sideways from central states has drawn attention from a growing number of scholars in political science. Yet beyond agreement that governance has become (and should be) multi-level, there is no consensus about how it should be organized. This article draws on several literatures to distinguish two types of multi-level governance. One type conceives of dispersion of authority to general-purpose, nonintersecting, and durable jurisdictions. A second type of governance conceives of task-specific, intersecting, and flexible jurisdictions. We conclude by specifying the virtues of each type of governance.For comments and advice we are grateful to Christopher Ansell, Ian Bache, Richard Balme, Arthur Benz, Tanja Börzel, Renaud Dehousse, Burkard Eberlein, Peter Hall, Edgar Grande, Richard Haesly, Bob Jessop, Beate Kohler-Koch, David Lake, Patrick Le Galés, Christiane Lemke, David Lowery, Michael McGinnis, Andrew Moravcsik, Elinor Ostrom, Franz U. Pappi, Thomas Risse, James Rosenau, Alberta Sbragia, Philippe Schmitter, Ulf Sverdrup, Christian Tusschoff, Bernhard Wessels, the political science discussion group at the University of North Carolina, and the editor and three anonymous reviewers of APSR. We received institutional support from the Center for European Studies at the University of North Carolina, the Alexander von Humboldt Foundation, and the Wissenschaftszentrum für Sozialforschung in Berlin. Earlier versions were presented at the European Union Studies Association meeting, the ECPR pan-European Conference in Bordeaux, and Hannover Universität, Harvard University, Humboldt Universität, Indiana University at Bloomington, Mannheim Universität, Sheffield University, Sciences Po (Paris), Technische Universität München, and the Vrije Universiteit Amsterdam. The authors' names appear in alphabetical order.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".