Searching and Seizing After 9/11: Developing and Applying\nEmpirical Methodology to Measure Judicial Output inthe\nSupreme Court's Section 8 Jurisprudence
Bibliographic record
Abstract
In 2005, Margit Cohn and Mordechai Kremnitzer created a multidimensional model to measure judicial discourse inherent in the decision making of constitutional courts. Their model set out multiple indicia bywhich to measure whether the court acted within proper constitutional constraints in order to determine the extent to which a court rendered a decision that was activist or restrained. This study attempts to operationalize that model. We use this model to analyze changes in interpretation of search and seizure law under section 8 after the enactment of the Canadian Charter of Rights and Freedoms at the Supreme Court of Canada. The authors attempt to determine whether or not there were significant changes in the levels of measurable judicial discourse after 9/11. They explain how the model can be adapted into a Canadian context and justify the adapted model. The last part of the paper undertakes the application of the model to all Supreme Court cases since 1982 that explored Charter-based search and seizure issues. Ultimately, the paper finds significant changes in judicial discourse for certain types of judicial output, which indicate a more conservative approach to judicial decision making in the period after 9/11. The adapted model serves as a reminder that courts exercise their decision making through discourse that moves in numerous directions in any given era and that likely does so differently in alternate areas oflaw. Future research applying the Cohn/Kremnitzer model promises rich, complex analysis that will serve to enrich our understandings of law and society
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".