Bibliographic record
Abstract
The relationship between fault and ultra vires is one of the most difficult aspects of the law of Crown Liability. It sets clearly into relief the policy conflicts which arise when private law risk allocation regimes (the adversarial adjudicative imposition of liability rules grounded in a concept of corrective justice) are invoked to police the functioning of public law risk allocation regimes (the allocation through various non-adjudicative procedures of the benefit and burden according to a variety of conceptions of distributive justice). The Crown Liability Act and article 94 of the Code of Civil Procedure both incorporate as against the Crown rules of private law delictual behaviour which were originally developed for regulating activity between private parties as such. They, therefore, compel courts to determine whether jurisdictional error per se constitutes fault. The history of twentieth century attempts to reconcile ultra vires and fault is a history of the judicial search for boundary criteria between realms of public and private law. These boundaries have been, among others, a good faith test, functional criteria such as judicial and legislative immunity or immunity for planning functions, the notion of breach of statutory duty, and so on. Each of these attempts has ultimately be repulsed by the desire of litigants to recover against the Crown on the widest possible basis. Modern theories of jurisdiction being so all-embracing and modern conceptions of fault being so comprehensive, the courts are constantly being asked to develop an absolute equation between fault and ultra vires. The paper concludes by exploring several options for harmonizing private law and public law risk allocation regimes. It recommends a restructuring of the Crown Liability Act so as (i) to permit recovery on a variety of no fault bases, (ii) to permit recovery even when intra vires acts have been undertaken (if these cause significant or disproportional damage) and (iii) to permit the immunization of certain governmental functions from private law liability even when the decisions in question have been taken in an ultra vires fashion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".