Bibliographic record
Abstract
Abstract This book provides the first ever in-depth treatment of shared rule, a crucial but so far largely neglected dimension of federalism and multilevel governance. The book discusses shared rule’s conceptual evolution and defines three different meanings commonly ascribed to it: shared rule as horizontal cooperation, centralization, or bottom-up influence-seeking. An original expert survey conducted among thirty-eight federalism scholars in eleven countries is used to measure actual as opposed to merely potential regional government influence over national decisions. Drawing on a wide range of literature, from lobbying and political parties to power-sharing and secessionism, the book then investigates the emergence and impact of shared rule thus understood. The evidence presented includes qualitative case studies on Belgium, Canada, Germany, Spain, Switzerland, and the USA as well as quantitative, cross-sectional analyses at regional and national level. The book shows that shared rule has the potential to become the holy grail of territorial politics in that it satisfies both those wanting greater unity and uniformity of policy-making and also those desiring greater regional autonomy and recognition of diversity. Building on the conceptual and empirical groundwork laid by the Regional Authority Index, the book thus takes us further and deeper into the mechanics of territorial contestation, cooperation, and cohesion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".