Bibliographic record
Abstract
Abstract This chapter discusses the origins and growth of judicial review in the United Kingdom of Great Britain and Northern Ireland. Judicial review in the United Kingdom under the Human Rights Act is best explained by borrowing from the United States, Canada, Germany, and the European Court of Human Rights. The emergence of judicial review in the United Kingdom also coincided with the devolution of power to Scotland, Wales, and Northern Ireland, thus creating a need for a federalism umpire. This was vividly illustrated by a recent U.K. Supreme Court separation of powers umpiring opinion; and by a 2019 umpiring ruling, which upheld Scotland’s highest court, while overturning an English and Welsh court on the justiciability and breadth of The Queen’s power to prorogue Parliament. The adoption by the United Kingdom of the European Convention on Human Rights (ECHR), as a judicially enforced Bill of Rights, was done, in part, out of embarrassment that the United Kingdom kept losing so many human rights cases when they were heard by the European Court of Human Rights (ECtHR). There is, accordingly, a mild rights from wrongs story that explains the adoption of the Human Rights Act of 1998, although a desire to borrow that which was fashionable and in style provides the major explanation for the adoption of this act.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".