The Supreme Court of Canada and Mainstreamed Judicial Analytics
Bibliographic record
Abstract
This chapter explores how mainstreamed judicial analytics might impact the Supreme Court of Canada. Specifically, the chapter explores how analytics could influence: (1) the appointment process for Supreme Court judges; (2) the adjudication of cases at the Supreme Court; and (3) the ability of the public – and the Court itself – to appraise trends and tendencies in judicial decision-making at the Supreme Court. After canvassing the opportunities and limitations of utilizing judicial analytics in these three contexts, the chapter concludes that, subject to some important limits, analytics may contribute to improved knowledge and transparency about the Supreme Court&s;s work and may provide new avenues for increased accountability. At the same time, the chapter highlights key risks of relying on judicial analytics tools to understand the work of the Court and its judges. To minimize such risks, high-quality tools must provide appropriately contextualized outputs, and stakeholders, including the Court and its judges, should ensure they understand analytics tools and their outputs.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.024 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".