Judicial Review of Executive Policymaking in Advanced Democracies: Beyond Rights Review
Bibliographic record
Abstract
The legitimacy of modern states depends on the ability of democratic institutions to reflect citizens' preferences and values and on the state's ability to use technical expertise competently. Legitimacy has a three-fold character based on rights, democratic responsiveness, and competence. We argue that courts can help reconcile these competing aspects of executive legitimacy. Our premise may seem implausible because courts are the archetypal counter-majoritarian institution, and judges typically have little knowledge of technical subjects. However, based on a critical review of the law in the United States, Canada, Italy, and France, we argue that courts can balance respect for democratic choice and deference to experts with limited oversight that enhances legitimacy across all three dimensions. We discuss the hazards of substantive review by technically illiterate courts and argue that procedural review can be a partial substitute. If courts review rulemaking, they need to acknowledge its role in upholding policymaking values, and if they review adjudications, they need to understand that court-like procedures are inadequate to capture the broad policy issues often at stake. Based on our review of the four case studies, we conclude that to further both democracy and competence, courts: (i) should review the substance of the agencies' decisions under a weak reasonableness test and (ii) should concentrate on the administrative process, notably by enforcing a widespread duty to give reasons and by assuring generous rights of participation.
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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 | 0.072 | 0.177 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| 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".