LESSONS FROM LATIF: GUIDANCE ON THE USE OF SOCIAL SCIENCE EXPERT EVIDENCE IN DISCRIMINATION CASES
Bibliographic record
Abstract
The Supreme Court of Canada’s decision in Latif is important not only for its clarification of the test for establishing prima facie discrimination in human rights cases, but also for its guidance on the use of social science expert evidence in discrimination cases. This article examines the Supreme Court’s decision in Latif, with a particular view to identifying lessons for applicants seeking to establish discrimination via social science expert evidence. In particular, we argue that litigants adducing social expert evidence should ensure to: (a) carefully explain the relevance of the social science expert evidence and link the social science expert evidence to specific material issues in the case; (b) spell out the chain of inferences they wish to draw from circumstantial evidence and explain how the expert evidence increases the strength of those inferences; (c) link the expert evidence to the respondent’s lack of a justification; (d) address why expert evidence on a material issue is unavailable (if that is the case); and (e) consider adducing statistical evidence of discrimination when possible.
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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 | 0.110 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.041 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".