Shadow constitutional review: The dark side of pre-enactment political review in Ireland and Japan
Bibliographic record
Abstract
Abstract Political constitutionalism is a major area of inquiry in contemporary constitutional discourse. A significant and increasingly central aspect of political constitutionalism is pre-enactment political review: laws being reviewed for constitutionality or rights compliance by parliament or the executive. This institution is said to be a good augmentation of, or even replacement for, the institution of judicial review, and it is said to bring with it a host of normative benefits. In this article, we wish to highlight an under-explored dark side to pre-enactment review. By undertaking a comparative analysis of functional pre-enactment review in several similar jurisdictions—Canada, New Zealand, and the UK—we contrast these systems, and the ordinary failings they display, with the much deeper problems of pre-enactment review in Ireland and Japan. These latter jurisdictions, we argue, not only fail to instantiate the benefits of pre-enactment review but in fact show that, in the right circumstances, pre-enactment review can have negative effects that are antithetical to the goals and values of political constitutionalism. We call this phenomenon “shadow constitutional review,” and suggest that it adds a layer of complexity and nuance to contemporary discussions of political constitutionalism.
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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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 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".