Bibliographic record
Abstract
Abstract This concluding chapter identifies the four major causes of the growth and origin of judicial review in the G-20 common law countries and in Israel. First, the need for a federalism umpire, and occasionally a separation of powers umpire, played a major role in the development of judicial review of the constitutionality of legislation in the United States, in Canada, in Australia, in India, and most recently in the United Kingdom. Second, there is a rights from wrongs phenomenon at work in the growth of judicial review in the United States, after the Civil War; in Canada, with the 1982 adoption of the Canadian Charter of Rights and Freedoms; in India, after the Indira Gandhi State of Emergency led to a massive trampling on human rights; in Israel, after the Holocaust; in South Africa, after racist apartheid misrule; and in the United Kingdom, after that country accumulated an embarrassing record before the European Court of Human Rights prior to 1998. This proves that judicial review of the constitutionality of legislation often occurs in response to a deprivation of human rights. Third, the seven common law countries all borrowed a lot from one another, and from civil law countries, in writing their constitutions. Fourth, and finally, the common law countries all create multiple democratic institutions or political parties, which renders any political attempt to strike back at the Supreme Court impossible to maintain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.004 |
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".