Non-hierarchical policy coordination in multilevel systems
Bibliographic record
Abstract
In theory, lower-level governments (provinces, regional governments, or member states) operating in multilevel systems within and beyond the nation-state can choose from a wide repertoire of modes of policy coordination to solve collective problems non-hierarchically. These modes range from unilateral policy emulation over informal intergovernmental agreements to binding interstate law. The modes that governments arewillingandcapable to use, however, vary considerably across multilevel systems which affects governments’ collective problem-solving capacity. This paper argues that the nature of executive–legislative relations in lower-level governments is crucial to account for this variation. The presence (or absence) of power sharing shapes thewillingnessof lower-level governments to enter agreements that greatly constrain individual government autonomy. Power-concentrating governments, as opposed to power-sharing ones, tend to avoid such agreements. Thetypeof power sharing affects thecapacityto enter agreements that require legislative approval. Compulsory power-sharing governments, as opposed to voluntary power-sharing governments, should find it difficult to enter such agreements, since this type of power sharing invites inter-branch divides. To substantiate these arguments, we apply them to Canada, Switzerland, the United States, and the European Union.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".