Rethinking the Process/Substance Distinction: Baker V. Canada
Bibliographic record
Abstract
The authors argue that in Baker v. Canada, the Supreme Court of Canada set out a unified theory of judicial review which seeks to rest review on substantive ideals related to fundamental values such as human rights and the best interests of children. The Court's reliance on substantive criteria to guide review places stress on the traditional process/substance distinction under which reviewing courts would generally review procedural matters with some intensity, but would resist reviewing the substance or merits of a decision with the same intensity. The authors argue that the newfound stress on the process/substance distinction is welcome, and to some extent can be mitigated, because the reasons underlying review of procedure equally justify review of cases in which human rights and other fundamental values are at stake.
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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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".