Bibliographic record
Abstract
In Canadian public law, the foundational case of Roncarelli v. Duplessis stands for the proposition that arbitrariness and the rule of law are conceptually antithetical values. This article examines multiple forms of arbitrariness in Roncarelli, going beyond the usual focus on discretionary power arbitrarily exercised by the executive branch of government. A close reading of the case not only brings to the surface other forms of arbitrariness, notably under-acknowledged forms of judicial arbitrariness, but also illuminates how legal actors attempt to constrain arbitrariness within the activity of judging. Furthermore, repositioning the case in its larger social and political context provides an alternative vantage point from which the core conceptual content can be enlarged and the case’s normative import better gleaned. Indeed, what is most surprising about Roncarelli is how well it tracks the meanings and attributes of arbitrariness identified by legal theorists such as Joseph Raz, Henry Richardson, Jeremy Waldron and others. Legal theory and practice both confirm reason-giving as one significant rule of law practice that constrains arbitrariness by seeking to ensure that decision-makers throughout the state are attuned to the demands of legality, can be publicly held to account, and are committed to upholding principles of good government.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".