Revisiting and Understanding Crown Liability from a Historical Constitutional Perspective
Bibliographic record
Abstract
Abstract In this chapter, the evolution of the understanding of the king can do no wrong in light of this book’s model is outlined. A discussion of current legal thinking in relation to crown liability is presented, informed by the historical constitutional approach adopted in this book. An analysis of the king can do no wrong as an ahistorical basis for crown immunity is conducted. Dicey’s influence on the current understanding of crown liability and on the Crown Proceedings Act 1947 (and its Canadian counterpart, the Crown Liability and Proceedings Act) is discussed. A possible reorientation of our understanding of the crown and of crown liability is suggested, based on a historically informed understanding of the king can do no wrong. The chapter concludes by highlighting the divergence in constitutional thinking in the realms of judicial review, the prerogative, the law of public authorities’ liability, and crown liability.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".