Bibliographic record
Abstract
In his provocative and masterly book Judging Under Uncertainty Adrian Vermeule seeks to displace the dominance of what he calls first-best conceptualism in legal theory and instead argues that interpretive law needs to take an institutional turn. Vermeule's focus on the empirical problems of institutional interpretation is a welcome and long overdue contribution to legal theory. Judging Under Uncertainty is an ambitious book and a valuable contribution to legal theory. The book deals exclusively with American law and Vermeule's institutional approach to legal interpretation takes the existing status quo of the American system as a given. This, one might be tempted to complain, limits the scope and application of the institutional theory advanced by Vermeule. I shall raise some of these concerns towards the end of this review article when I consider some of the insights Canadian jurisprudence could contribute to the development on an institutional theory of legal interpretation (as well as the insights Canadian legal theorists can take from Vermeule's compelling and important arguments).
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.075 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| 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".