Bibliographic record
Abstract
The Road Ahead We have now examined the Standard Case for judicial review. As we have seen, a number of different arguments figure in this case. For example, judicial review is often applauded for the protections it is said to afford minorities or individuals whose moral rights are threatened by government action or inaction, and for the help it provides in securing certain fundamental conditions of a thriving democracy. Sometimes these conditions are thought to derive from the constitutional conception of democracy. At other times they are said to follow from a less ambitious procedural conception. But judicial review is not without its detractors. Many public figures and scholars – whom we lumped together and dubbed “the Critics” – are equally adamant in condemning judicial review on a wide range of philosophical and practical grounds. Because the principal aim of this book is a plausible defence of Charters and the practices of judicial review to which they give rise, it is incumbent on me to explore and answer the Critics' most important objections. This I shall begin to do in the present chapter. In defending a practice, the best strategy is usually to address the arguments of its strongest critic, and it is for this reason that we will be focusing, though not exclusively, on the work of Jeremy Waldron, who, in two important books, The Dignity of Legislation and Law and Disagreement , argues strenuously against Charters and their enforcement by judges.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".