The use of social science evidence in constitutional adjudication: overcoming the challenges of the adversarial system
Bibliographic record
Abstract
This thesis examines the practice of judicial reliance on social science evidence in the context of Canadian Charter litigation. It undertakes in-depth readings of two recent trial decisions dealing with prostitution and polygamy, which required the judges to analyze vast amounts of social science empirical data. The argument is that the legal system's prioritization of persuasion, victory and the definitive resolution of disputes prevents it from maximizing the potential contributions that the social sciences can bring to the law and the legal search for truth. The doctrine of stare decisis may also require rethinking. This thesis also explores the idea that adversarial adjudication is ill suited to the balancing of a variety of unsettled issues often required by Charter challenges. This difficulty is compounded by the demonstrated weaknesses of legal education and its failure to equip future lawyers and judges with the non-legal skills required to deal with complex and conflicting empirical data. Last, the thesis looks at another major flaw in Anglo-American adjudication, the party selection of expert witnesses and the necessary bias which results, providing an overview of alternative procedural mechanisms. Overall, the difficulties in combining the law and the social sciences can only be remedied by moving towards a more inquisitorial method of resolving constitutional disputes.
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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 | 0.222 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.017 | 0.109 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.011 |
| 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".