Bibliographic record
Abstract
Constitutional Statutes seem to describe an oxymoron that challenges the traditional hierarchical dichotomy between regular statutes and constitutional provisions. Constitutional Statutes might mean different things to different political actors (including judges) and academics within the same legal system and across countries. This article argues that we should analyze constitutional statutes along two vectors: what makes these statutes constitutional (identification) and what are the ramifications of such identification (consequences). It further argues that the answer to the first question affects the results of the second. The article argues that statutes are identified as constitutional based on either the process of their enactment or their content, which may itself be subdivided into two classifications: content in terms of importance and content in terms of entrenchment language. Constitutional statutes discussed in the literature and jurisprudence are often treated as though they are made of one cloth, when in fact they belong to different categories based on the justification for, as well as ramifications of, their constitutionality. The article’s approach is examined with relation to the U.S., U.K., Canada, Israel, New Zealand and Australia.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".