Bibliographic record
Abstract
Roncarelli v. Duplessis figures far more frequently in Australia’s secondary literature than in its court decisions, and it is noted not for its invalidation of Prime Minister Duplessis’s actions, but for its award of damages where judicial declaration of invalidity would usually be the only remedy. Invalidating Duplessis’s interference with Roncarelli’s liquor licence would have been the easy part of the case had it been tried in Australia. Australian statutes afforded good protection to liquor licensees, and general administrative law principles confined seemingly unfettered discretionary powers in less solicitous statutory regimes. In addition, the constitutional abolition of internal trade barriers used to be taken as banning unfettered regulatory powers over interstate traders. Duplessis’s tort liability was the hard part. His assumption of legal power was not deliberate, but it was extraordinarily indifferent to questions of legality. Justice Rand characterized this as “malice”, which in turn triggered liability to a uniquely public law tort known nowadays as misfeasance in public office. That tort is likely to cover more forms of non-deliberate official misconduct in Canada than in Australia, whose High Court usually avoids open-ended legal principles, particularly those according immediate operative force to substantive conceptions of the rule of law.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".