Bibliographic record
Abstract
This Critical Notice deals with two recent books that report the findings of statistical analyses of Supreme Court of Canada judgments over extensive periods of time. The authors of both studies argue that empirical analysis can make significant contributions to theories of law and the understanding of what high court judges do, and in fact, that theoretical approaches on their own are necessarily inadequate to this task. The review questions whether the studies succeed in meeting the ambitions set for them. In Attitudinal Decision-making in the Supreme Court of Canada, Ostberg and Wetstein fail to establish that the attitudinal model of jurisprudence developed by American political science provides a strong explanation of the performance of the Supreme Court of Canada. In The Empirical Gap in Jurisprudence, Daved Muttart employs such broadly stated measures of judicial reasoning that his conclusions about the Court’s performance remain general in nature and do not pose serious challenges to the major, competing schools of jurisprudential thought he seeks to examine. Both studies fall back on unconvincing arguments about the prevalence of judicial activism in Supreme Court decision-making in the absence of stronger findings on their principal themes.
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.
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 | 0.052 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.007 | 0.076 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".