Judging by the Numbers: Judicial Analytics, the Justice System and its Stakeholders
Bibliographic record
Abstract
This article considers the future of judicial analytics, its possible effects for the public, the judiciary and the legal profession, and potential responses to the rise of judicial analytics in Canada. Judicial analytics involves the use of advanced technologies, like machine learning and natural language processing, to quickly analyze publicly-available data about judges and judicial decision-making. While, in Canada, judicial analytics tools are as yet at the early stages of development and use, such tools are likely to become more powerful, more accurate and more accessible in the near-to-medium future, resulting in unprecedented public insight into judges and the work of judging. This article identifies benefits of mainstreamed judicial analytics, including increased transparency into the work of judging, and risks flowing from the rise of judicial analytics, including the propagation of inaccurate or misleading information about judges. In light of these benefits and risks, the article identifies voluntary third-party certification and the production of credible public tools as meaningful responses to the rise of judicial analytics and calls on judicial regulators to consider how information made available through judicial analytics tools may influence their work.\nCet article examine l’avenir de l’analyse judiciaire, ses effets possibles sur le public, la magistrature et la profession juridique, et les réponses possibles à la montée de l’analyse judiciaire au Canada. L’analyse judiciaire implique l’utilisation de technologies avancées, comme l’apprentissage automatique et le traitement du langage naturel, pour analyser rapidement les données accessibles au public au sujet des juges et des décisions judiciaires. Bien qu’au Canada, les outils d’analyse judiciaire n’en soient encore qu’aux premiers stades de développement et d’utilisation, il est probable que ces outils deviendront plus puissants, plus précis et plus accessibles dans un avenir proche ou moyen, ce qui permettra au public d’avoir une vision sans précédent des juges et de leur travail. Cet article identifie les avantages de l’analyse judiciaire généralisée, notamment la transparence accrue du travail des juges, et les risques découlant de l’essor de l’analyse judiciaire, notamment la propagation d’informations inexactes ou trompeuses au sujet des juges. À la lumière de ces avantages et de ces risques, l’article identifie la certification volontaire par une tierce partie et la production d’outils publics crédibles comme des réponses significatives à l’essor de l’analyse judiciaire et appelle les régulateurs judiciaires à considérer comment les informations rendues disponibles par les outils d’analyse judiciaire peuvent influencer leur travail.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".