Insurance and human rights: what can Europe learn from Canadian anti-discrimination law?
Bibliographic record
Abstract
There has been considerable debate recently in Europe over the question whether and to what extent anti-discrimination provisions ought to protect people in the context of insurance. This chapter looks at how developments in Canadian anti-discrimination law could provide inspiration in the context of this debate. The chapter first sketches a picture of Canadian equality law as it applies to insurance contracts. It notes two important developments that characterize Canadian law: the abandonment of the distinction between direct and indirect discrimination; and the emphasis, under a substantive equality approach, on the duty to accommodate. The chapter then discusses in detail how developments in Canadian equality law will likely affect a court's approach towards discrimination in the insurance context. The authors argue that as a result of decisions by the Canadian Supreme Court related to discrimination outside the context of insurance, insurance companies will face a higher burden of proof to justify distinctions made in the context of insurance. The chapter looks in detail at some of the recent case law in Canada which supports this argument. In the conclusion, the authors suggest how this approach could be used in the European context.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".