Quantifying <i>Dunsmuir</i>: An empirical analysis of the Supreme Court of Canada’s jurisprudence on standard of review
Bibliographic record
Abstract
In this empirical study, the author assesses an argument advanced by several scholars that the framework for the selection of standards of review articulated by the Supreme Court of Canada in Dunsmuir v New Brunswick leads to less deference being shown by judges to administrative decision makers than the prior pragmatic and functional approach. An examination of the Court’s voting record in 177 cases dating back to Pushpanathan v Canada suggests that, to the contrary, members of the Court have shown greater deference to administrative decision makers in the years since Dunsmuir was decided than they did under the prior framework. For example, the rate at which the correctness standard was selected after a standard of review analysis was undertaken decreased from 43 per cent before Dunsmuir to 17 per cent in subsequent years. The rate at which members of the Court voted to overturn administrative decisions after identifying the applicable standard decreased from 38 per cent before Dunsmuir to 23 per cent thereafter. While a multiple regression analysis to control for confounding factors was not undertaken, these changes appear to flow from a change in the Court’s approach rather than from factors such as changes in the composition in the Court or changes in the kinds of cases that were heard after Dunsmuir. The author suggests, however, that this shift in approach is not necessarily inherent to the Dunsmuir framework itself and that there are signs that the Court may have already begun to adopt a somewhat less deferential posture.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".