CAN INSURANCE LAW ACCOMODATE THE UNCERTAINTY ASSOCIATED WITH PRELIMINARY GENETIC INFORMATION
Bibliographic record
Abstract
This paper discusses first how the results of genetic testing could be used in the context of life insurance contracts. It then highlights how current insurance law offers some protection against inappropriate use of genetic information. On the basis of an analysis of Canadian case law, the argument is made that insurance law does leave room for policy considerations, particularly through flexibility in the interpretation of the mutual good faith obligations of insurers and insurance applicants. The courts' decisions often reflect concerns for fairness which differ from the actuarial fairness concept supported by insurance companies. The paper also recommends changes to insurance law to strengthen the protection against premature or inappropriate use of genetic and other health information.
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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 | 0.069 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.018 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".