Judicial Institutionalization and Judicial Activism of the Post-Communist Constitutional Courts
Bibliographic record
Abstract
In applying constitutional review, post-communist constitutional courts are affected by the existing political and institutional environments, as well as by their own institutional capabilities. However, our understanding of the activity of the post-communist constitutional courts remains incomplete because the existing research fails to consider how the institutional changes on these courts affect their decision-making behavior. In this study, I examine the activity of nineteen post-communist constitutional courts during the 1992-2006 period. I use an aggregate, time-series measure of judicial institutionalization to show that higher levels of institutionalization enhance these constitutional courts’ ability to pursue their policy goals and influence the degree to which they invalidate policy choices of other major political actors, while lower levels of institutionalization limit the courts’ impact on legal and political issues. The findings of this analysis thus provide the first empirical confirmation of the importance of judicial institutionalization to the policy outputs of the post-communist constitutional courts. I also illustrate how various institutional and contextual influences, such as executive power, legislative fragmentation, economic conditions, EU accession process, the identity of the litigants, and the nature of the litigated issues, influence the activity of post-communist constitutional courts.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".