Bibliographic record
Abstract
The author traces 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 the rights, interests, or privileges of an individual. The threshold tests for determining when fairness is required are analyzed. Particular attention is paid to the concept of legitimate expectation, which may expand the availability of the fairness duty as a result of the conduct of public officials. Various limitations on the duty of fairness are considered, the most controversial of which is the non-application of the duty to legislative decisions. The duty of fairness has remained confined to procedural protection in Canada, and the content of the duty is context dependent. It may be satisfied by minimal, informal procedures in some cases - perhaps notice and a chance to reply - while in other cases nothing less than a formal, oral hearing will do. The criteria established by the Supreme Court of Canada in Baker v. Canada (Minister of Citizenship and Immigration) for determining the degree of fairness required in particular circumstances are discussed at length.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.083 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".