The Supreme Court of Canada and Mainstreamed Judicial Analytics
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
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 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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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