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Record W4395059230 · doi:10.29173/alr2713

The Most-Cited Law Review Articles of All Time by the Supreme Court of Canada

2022· article· en· W4395059230 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAlberta Law Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSupreme courtLawPolitical science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.269
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it