Decoding the Court: Legal Data Insights from the Supreme Court of Canada
Bibliographic record
Abstract
This edited collection combines state-of-the-art legal data analytics with in-depth doctrinal analysis to study the Supreme Court of Canada (SCC), Canada’s top court. A data analytics perspective adds new dimensions to the study of courts and their case law. It renders legal analysis scalable, making it possible to investigate thousands of judicial decisions, adding new breadth and depth. It also enables researchers to combine doctrinal questions about how the law evolves with institutional questions about how courts operate, shedding new light on how law works in practice. By applying a range of methods to study the content of SCC decisions, this work bridges the gap between qualitative and quantitative research. Demonstrating how new analytical perspectives can generate new insights about the Supreme Court, an institution which is closely studied by scholars both within and outside Canada, the book will be essential reading for legal scholars and political scientists, particularly those working in public law and in empirical legal studies. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".