Formal and Informal Institutions Under Codecision: Continuous Constitution‐Building in Europe
Bibliographic record
Abstract
Current approaches examining the effect of institutions on policy processes have difficulty in explaining the results of the legislative process of codecision between the European Parliament and Council within the European Union. The formal Treaty changes that gave rise to codecision have, in turn, given rise to a plethora of informal institutions, in a process that is difficult to understand using dominant modes of analysis. This article provides a framework for analyzing the relationship between formal and informal institutions, showing how the two may be recursively related. Formal institutional change at a particular moment in time may give rise to informal institutions, which may, in turn, affect the negotiation of future formal institutions. The article applies this framework to the codecision process, showing how the codecision procedure has led to the creation of informal institutions and modes of decision‐making, which have affected subsequent Treaty negotiations. Through strategic use of the relationship between formal and informal institutions, the European Parliament has been successful in advancing its interests over time and increasing its role in the legislative 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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".