Judging the Social Sciences in Carter v. Canada
Bibliographic record
Abstract
This paper examines a recent example of evidence-based decision making affecting social policy at the trial court level. It offers a close reading of Carter v Canada (AG), decided by the British Columbia Supreme Court, and of Justice Lynn Smith’s careful scrutiny of the social science evidence when invalidating the Criminal Code prohibition on assistance in dying. Drawing on literature which examines the legal system’s use of social science evidence and expert witnesses, this paper suggests that Justice Smith’s treatment of the evidence in Carter provides an example of skilled judicial treatment of the extensive amounts of social science evidence typically tendered in Charter challenges related to controversial social issues. First, it considers the implications of the Supreme Court of Canada’s revised approach to social fact-finding by trial judges and the consequent need for trial judges to critically evaluate and effectively draw on the social sciences. Second, it examines certain limits to courts’ institutional capacity to evaluate the work of social scientists – specifically, the general lack of judicial training in disciplines other than law – and suggests that the trial judge’s approach in Carter is one to be emulated in future cases with similarly vast evidentiary records. Third, it looks at the role of the expert witness and at some of the dangers inherent in judicial reliance on expert testimony and highlights the ways in which Justice Smith’s careful consideration of the subtle effects of adversarial bias may have affected her approach to the evidence. It suggests that while some judges might struggle with common risks and challenges associated with judicial reliance on this type of evidence in the adjudication of social policy, the trial decision in Carter demonstrates that these difficulties may be overcome.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.017 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".