The Most-Cited Law Review Articles of All Time by the Supreme Court of Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scholars use citation counts to measure the impact of scholarly works in a wide range of disciplines, including law. The aims of this study are twofold: to present the methods most commonly used to measure the impact of scholarly works and to determine which law reviews and articles the Supreme Court of Canada has cited most since its creation. Part II of this study reveals that legal scholars typically use three methods to generate lists of important works: the periodical citation method; the judicial citation method; and the peer rating method. The choice of method depends on the research objective. Part III of this study adopts the judicial citation method to identify the law reviews and articles most cited by the Supreme Court and provides a qualitative analysis of the top three articles. It focuses solely on publications in generalist, peer-reviewed, and university-based law reviews that were created in or before 1982. This study finds that two law reviews — the McGill Law Journal and the University of Toronto Law Journal — and 39 articles have been particularly successful. These articles were predominantly written in English by male law professors holding degrees from elite law schools and concern pressing constitutional law issues. As society shifts to tackle biases in all professions, including academia and law, the attributes of the most-cited articles can be expected to evolve — and the gender gap to close — in the years to come.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it