Bibliographic record
Abstract
Abstract Suppose individuals have information from genetic tests concerning their mortality or morbidity risk and insurers are not allowed access to it. Higher risks will view insurance as a ‘better deal’ than will lower risks when the price is the same for all and so higher risk consumers will be inclined to purchase more insurance than those with lower risk. The result will be upward pressure on the average market price for insurance. Insurers will react by either raising price or tailoring insurance policies to separate the two types of consumers. In this article, the implications on consumer well‐being in such an economic environment are described and compared to the outcome where insurers are allowed to use the results of genetic tests to price insurance accordingly. Key Concepts: If insurance companies do not have access to genetic test results of their potential customers, then this creates a situation of asymmetric information and a problem of adverse selection. Since ‘bad risks’ will desire more insurance than ‘good risks’ if the price is the same for all, then insurers would end up selling more insurance to the bad risks and so the overall price of insurance may rise as a result. Adverse selection may also result in screening strategies whereby insurers offer higher coverage at a higher unit price in order to attract high risk types with lower risk types enduing up with less insurance coverage. Allowing insurers access to genetic test results leads to greater efficiency of the insurance market but may also create a potential genetic underclass; that is, people who face very high prices for insurance or no insurance at all. Economic models of insurance markets allow one to understand the tradeoffs between effective (efficient) operation of insurance markets and equity concerns.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".