Bibliographic record
Abstract
Abstract The potential use of genetics for insurance purposes raises concerns about genetic discrimination. Genetic discrimination refers to the unfair treatment of individuals based on their genetic information as opposed to their physical features. Regulatory strategies to curb genetic discrimination exist in several countries. These regulations aim at promoting a just distribution of insurance, which has multiple meanings in different social contexts, and at curbing the potential negative impact of genetic discrimination. In the United States, for example, genetic discrimination laws aim at preventing genetics from augmenting the already existing inequity of access to healthcare. Many European countries, for their part, seem to consider that some form of financial security offered by private insurance should also be accessible independent of one's genes. Finally, protection against genetic discrimination can sometimes be placed in a larger social context, where insurance is a necessary condition for access to other goods, towards citizens' equal participation in society. Key Concepts With the development of more predictive and cheaper genetic testing, insurers are increasingly interested in the risk information provided by this technology. Insurers are concerned that restrictions on accessing genetic test results obtained by insurance applicants will lead to adverse selection. lncreasingly, it is becoming clear that proteins encoded by ATG genes carry out functions in cellular pathways independent of their roles in autophagy. Adverse selection costs associated with restricting the use of genetic tests or access to test results may be modest, but may be more serious as more test results associated with serious risk factors become available. Regulations aimed at curbing genetic discrimination reflect a particular view about the social good nature of specific forms of insurance. Genetic discrimination statutes have particularly flourished in the United States because of the concern about the impact of the use of genetic testing on access to health insurance. Regulatory restrictions and moratoria on the use of genetic testing in countries with universal health insurance reflect the view that other types of insurance, such as life insurance, can fulfill a particular social goods function.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.039 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".