Bibliographic record
Abstract
Recognizing rationality It is still the case today that the most sustained attempt to understand judicial review for jurisdictional error as a legal phenomenon occurred in a series of articles, starting in the 1920s and finishing in the 1970s, by D. M. Gordon, a lawyer who practised in British Columbia. By legal phenomenon, I mean an attempt to understand such review within a coherent account of the rule of law. For it is easy to understand the political and other rationales for delegating authority to officials to implement public programmes – rationales to do with complexity, efficiency, and expertise. It is also easy to understand the reasons why governments think it necessary to protect public officials from the kind of judicial meddling which undermines the delivery of the statutory programmes the officials are charged with administering. In chapter 2, I discussed one of the main vehicles for protection, the privative clause which tells judges to refrain from review. But as we have seen, there are significant problems from the perspective of the rule of law for understanding the privative clause, which is why the evisceration approach developed in the United Kingdom, the approach which we saw simply empties a privative clause of all meaning. And, as we have also seen, the Australian attempt to take the privative clause seriously, as a legislative expansion of administrative jurisdiction, perches uneasily between evisceration and a rather different approach, the Canadian deferential approach.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".