LESSONS FROM LATIF: GUIDANCE ON THE USE OF SOCIAL SCIENCE EXPERT EVIDENCE IN DISCRIMINATION CASES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it