Bibliographic record
Abstract
Abstract: The issue of when, how and why the court may remedy the unconstitutional exercise of administrative discretion is largely unexplored. This study explores the relationship between discretion and the Charter and argues for a broader, more contextual approach to remedying the source of unconstitutional discretion. Guidance as to how to exercise broad discretionary authority comes in the form of “soft law,” which encompasses a variety of non‐legislative instruments such as policy guidelines and training materials, and which, more informally, extends to administrative culture. Administrative discretion involves choices and judgements usually shaped by a range of legal, bureaucratic, social and personal factors. Under present jurisprudence, the less precise a statutory discretion and the greater the reliance on non‐legislative guidelines, the more difficult that discretion will be to subject to constitutional scrutiny. This article challenges this logic and concludes that respect for governmental accountability and the rule of law require bringing soft law out of the constitutional shadows. The first part of the analysis examines the regulation of discretion generally and soft law specifically outside the Charter. The second part analyses the leading case law on the regulation of discretion under the Charter. The third section explores the intersection of discretion, soft law and the Charter. Finally, the fourth section considers the problem of remedying unconstitutional exercises of discretionary authority. Alternative principles are suggested for the development and application of soft law, which envisions a central role for the Charter in rendering the discretionary decision‐making process more accountable and just. A version of this paper was first presented at a workshop for the Twenty Years Under the Charter Conference, Association of Canadian Studies, Ottawa, 19 April 2002. The author is associate professor, Faculty of Law, University of Toronto. He is grateful to those who participated in that workshop for their suggestions and comments, as well as to Robert Chamey, David Dyzenhaus, Ian Greene, Nicholas Lambert, Ian Morrison and David Mullan, who commented on an earlier version of this paper. He is also indebted to his colleagues Sujit Choudhry and Kent Roach, who have shared their work on related themes. He would like to thank Laura Pottie and Aaron Delaney for their superb research assistance. He wishes to acknowledge the Social Sciences and Humanities Research Council, the Faculty of Law, University of Toronto, and the Connaught Foundation for their generous financial support of this research. Finally, he acknowledges the Journal's anonymous reviewers for their comments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".