THE REAL CASE FOR JUDICIAL REVIEW: A PLEA FOR NON- INSTRUMENTALIST JUSTIFICATION IN CONSTITUTIONAL THEORY
Bibliographic record
Abstract
This paper defends judicial review on the ground that judicial review is necessary for protecting “a right to a hearing.” Judicial review is praised by its advocates on the basis of instrumentalist reasons; i.e., because of its desirable contingent consequences such as protecting rights, promoting democracy, maintaining stability, etc. We argue that instrumentalist justifications for judicial review are bound to fail and that an adequate defense of judicial review requires justifying it on non-instrumentalist grounds. A noninstrumentalist justification grounds judicial review in essential attributes of the judicial process. In searching for a non-instrumental justification, we establish that judicial review is designed to protect the right to a hearing. The right to a hearing consists of three components: the opportunity to voice a grievance, the opportunity to be provided with a justification for a decision that impinges (or may have impinged) on one’s rights, and the duty to reconsider the initial decision giving rise to the grievance. The right to a hearing is valued independently of the merits of the decisions generated by the judicial process. We also argue that the recent proposals to reinforce popular or democratic participation in interpreting the Constitution are misguided because they are detrimental to the right to a hearing.
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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 | 0.104 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.065 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.029 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".