<scp>Regulating Genetic Information in Insurance Markets</scp>
Bibliographic record
Abstract
Abstract The debate on whether insurance companies should be allowed to use results of genetic tests for underwriting purposes is both lively and increasingly relevant as both technology and lawmaking efforts are progressing rapidly. In this article we outline the primary economic and non‐economic arguments made in favor of and against allowing insurers to risk‐rate premiums on the basis of genetic test results. While economic analysis has much to offer in enlightening this debate and informing policy makers, we argue that such work must be cast within the overall perspective of the genetic testing debate. Moreover, despite substantial strides by economists in understanding the role of information in the way insurance markets operate, much work still needs to be done in order for economic analysis to be confidently applied to the looming social issues of the continuing genetic revolution.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".