Genetic discrimination in private insurance: global perspectives
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
In an era of personalized medicine rife with population databases and international consortia, genetic discrimination is once again moving to the forefront of the genetics policy debate. In North America and Europe, many countries have taken a political stance on the use of predictive genetic information by insurers. Asia is also becoming more conscious of the challenge raised by genetic discrimination. In this paper, we present data on the different policy options adopted to resolve the genetic and insurance dilemma in 47 different countries located in four world regions. Approaches varied according to legal traditions, the role insurance plays in each state, and the interplay between private and public health care systems. We conclude that a truly informed international debate on genetic discrimination in insurance should properly account for the limits of genetic predictive information and the social value of health and life insurance as perceived by the public.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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