Mandatory pre-enactment review of legislation; or should only one government branch be responsible for the protection of rights in the United States?
Bibliographic record
Abstract
This paper focuses on pre-enactment review of legislation as a constitutional tool for the protection of recognised rights. The paper first makes the distinction between strong- and weak-form judicial review, in order to analyse how pre-enactment review can be practiced within each constitutional model. Two countries are first looked at to illustrate the two models: the United States for strong-form review, and New Zealand for weak-form review. The absence of any formal pre-enactment review in the United States is noted, and evaluated through a more in-depth assessment of congressional practice. This observation leads to the main proposal of the paper: that pre-enactment review should be made mandatory in the United States. A comparative assessment is then made in order to discuss the proposal. The relevant constitutional practices in Australia, Canada and Japan are outlined. These comparative assessments are used to further delineate the appropriate form that mandatory pre-enactment review of legislation could take in the United States.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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".