From Natural Justice to Fairness – Thresholds, Content, and the Role of Judicial Review
Bibliographic record
Abstract
The development of the “duty of fairness” is one of the great achievements of modern administrative law. It promotes a well-informed decision-making process, leading to better public policy outcomes, and at the same time helps to ensure that individuals are treated with respect in the administrative process.This chapter outlines the development of the duty of fairness in Canada beginning with Nicholson v Haldimand-Norfolk (Regional) Police Commissioners in 1979, in which the Supreme Court of Canada abandoned the dichotomy between judicial and administrative decisions, holding that a general duty of fairness applies whenever a decision is made that affects an individual's rights, privileges, or interests. The threshold tests for determining when fairness is required are analyzed and limitations on the reach of the duty of fairness are considered. The duty of fairness is context-specific, and considerations relevant to determining the content of the duty are addressed, along with particular requirements of the duty including the duty to provide reasons. The decision of the Supreme Court of Canada in Baker v. Canada (Minister of Citizenship and Immigration) is considered at length.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".