Constitutional review bodies’ orders to enact legislation: an introduction to the issue
Bibliographic record
Abstract
As a rule, a court decision’s value is in its implementation. Decisions of constitutional justice are no exception. The high mission of constitutional courts — bringing the legal system in line with the constitutional ideals — cannot be achieved without the participation of the legislature, which receives a large share of their rulings. It is logical that courts will seek to increase the effectiveness of enforcing their decisions by all available means. One such tool is an order to enact or to create legislation (normative order) which is an order made to the law-making body to amend legal rules, the aim is to remove a violation of the constitution. However, this part of constitutional court decisions usually goes unnoticed, not only in legal scholarship but also by the main addressee — the legislature. To gain a fuller understanding of normative orders, the author reviews constitutional courts’ jurisprudence in different legal families: common law (Canada, USA), civil law (Germany, Spain, Russia), and mixed systems (Israel, South Africa), also referring to legal doctrine, opinions of judges, and arguments of parties in court cases. The author focuses not only on the formal legal features of a normative order, but also on how it is developed inside the work of constitutional justice. The research shows that a normative order is an independent legal tool that serves different purposes than declaring a law unconstitutional, removing it from force, or interpreting it in a way consistent with the constitution. Normative orders can prevent future violations of the constitution and motivate the legislature to carry out the court’s decision, and most importantly, they can help the legal system develop. At the same time, the hypothesis that normative orders can only exist in a specific legal family or model of separation of powers is not confirmed. In reality, normative orders are very sensitive to various legal, political, and social factors, and identifying these factors requires separate research. Further study of normative orders, their origins, and their use in different countries could help address the larger problem of non-implementation or ineffective implementation of constitutional courts’ decisions.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".