Insuring Inequality: Sex-Based Mortality Tables and Women's Retirement Income
Bibliographic record
Abstract
This article discusses the legal status of the use of sex-based mortality tables (SBMTs) in annuitiy pricing, a practice still employed by the insurance industry in Canada and one which contributes to the very significant and persistent gender gap between the retirement incomes of men and women. In a 2011 decision in Association belge des Consommateurs Test-Achats ASBL v Conseil des ministres, the European Court of Justice ruled that SBMTs violate sex equality guarantees and can no longer be used by European insurers. The author argues that if SBMTs were challenged in Canadian courts, the result might well be the same. While the 1992 decision of the Supreme Court in Canada in the Zurich Insurance case has been widely interpreted as validating insurance rate classifications based on age and sex, the logic of Zurich Insurance may not shelter SBMTs in view of shrinking statistical differentials between the life expectancy of men and women, the existence of reliable gender-neutral annuity pricing tools and changes in the interpretation of legal equality guarantees since the early 1990s. She argues that pension laws and human rights codes which permit such practices may well fall afoul of the constitutional guarantee to equality under section 15 of the Canadian Charter of Rights and Freedoms.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".