‘Alert, alive and sensitive’: Baker, the Duty to Give Reasons, and the Ethos of Justification in Canadian Public Law
Bibliographic record
Abstract
This chapter argues that the remarkable phrase ‘alert, alive and sensitive’ – coined by Madame Justice L’Heureux-Dubé in the major Supreme Court of Canada decision, Baker v. Canada (Minister of Citizenship and Immigration) – signifies two important jurisprudential developments. First, the phrase ‘alert, alive and sensitive’ indicates a set of attributes connoting good judgment which can be used to evaluate the quality of judicial and administrative decisions. Second, the phrase comports with an emergent understanding of Canadian public law as an ‘ethos of justification’ in which citizens and non-citizens are democratically, and often constitutionally, entitled to participate in decisions made by government officials. Indeed, individuals are increasingly legally entitled to have access to and understand the reasons for these decisions as part of the content of the duty of fairness in administrative law. The chapter then analyzes the strong theoretical connections among the ethos of justification, crafting good judgments, and the duty to give reasons with the author arguing that the duty to give reasons rests on a normative foundation constituted by the values of dignity, rationality, and respect. This normative foundation structures the legal relationship between the individual and the state in Canadian public law. The author concludes that the ethos of justification could inform decision-making contexts outside of government.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".