Bibliographic record
Abstract
Social science evidence is the “new normal” in Charter litigation in Canada. Yet, the road is not smooth for the use of social science evidence in courts. How to effectively use such evidence remains an under-studied area. This paper explores the dynamics, strategies, and best practices associated with ad- ducing such evidence, using a qualitative, comparative perspective and examining cases from Canada, the United States, and South Africa. The paper argues that there are clear lessons emerging, in Canada and elsewhere, for how to effectively use social science evidence in constitutional cases. By analyzing scholarship and case studies in this area, the paper unpacks some of the dynamics and strategies at play for using social science evidence in courts. It puts forward five lessons, explaining that counsel who wish to harness social science evidence in Charter litigation should: (1) employ a group approach to constitutional litigation that brings together affected persons, community organizations, academics, and other experts; (2) present social science evidence early on in litigation and with the most reliable experts available; (3) ensure that social science evidence will withstand scrutiny under the applicable rules of evidence; (4) consider alternative strategies where social science evidence is weak, contested, or complex; and (5) prepare for a future where the importance of social science evidence in Charter cases increases.
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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 | 0.075 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.025 | 0.069 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| 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".