Bibliographic record
Abstract
Introduction The advent of genomics at the end of the 20th century intensified the existing debate surrounding the social, ethical, and legal issues raised by modern medicine. Genetics increased popular awareness of fundamentally important issues including the redefinition of the human being, eugenics and the organization of healthcare in liberal democracies. The general population, informed through the lens of the media, had to quickly learn and understand the meaning of complex terms and concepts such as cloning, stem cells, gene therapy, or medically assisted reproduction. The societal consequences of scientific developments in the field of genomics were especially visible in the areas of privacy and personal confidentiality. In the mid-1980s, progress in the development of predictive tests for Huntington's disease coincided with the first discussions in specialized fields about the risks of (1) The emerging debate, polarized by the media, became more extensive during the 1990s. Insurers' uncompromising attitude, (2) and the growing concern of the population about the vast possibilities presented by genetics, drove political decision-makers in many countries to legislate in order to address popular concerns. (3) However, despite intensive regulatory activity in Europe and in the United States, the socio-ethical debate on insurance and genetics has barely progressed during the last twenty years. After focussing on certain aspects of the genetics and insurance dilemma, this article will provide a critical analysis of the evolution of the debate in France and Canada. (4) Finally, the author will argue for the need to redefine the dialogue by broadening its scope, adopting a long term perspective and recognizing the need for transparency in order to exit the dead-end we are currently facing. In the current context, genetics could well be hiding a more fundamental debate about the place of insurance in the contemporary democratic state. Characteristics of the Debate The results of two recent North American surveys highlight the general discomfort caused by the fact that insurers could potentially use genetic information for underwriting purposes. (5) However, the two surveys share the same difficulty with regard to defining and circumscribing the precise extent of the popular sentiment. The first shows that although the majority of individuals surveyed oppose insurance companies' access to genetic data, these same individuals still perceive genetic information as being similar to other types of medical information and cannot articulate why it should be treated differently. (6) Although the results of the second study show a certain concern in the population vis-a-vis genetic discrimination, this concern seems to have had only a marginal influence on the surveyed population's decision to undergo genetic testing or not. (7) The discomfort reflected in these and other studies (8) could be explained by the difficulty of defining the concept of The concept of discrimination evokes a different reality for insurers, policy-makers, and the general population. Therefore, a single question about genetic discrimination may be perceived differently by various audiences. (9) For example, in order to assess if a given law or governmental practice is discriminatory or not, the Canadian judiciary will follow article 15(1) of the Canadian Charter of Rights and Freedom (10) and the corresponding case law. In this situation, discrimination is used to indicate a type of distinction that is both unjustified and based on prohibited (or analogous) grounds, i.e., unlawful discrimination. On the other hand, for an insurer, the selection and classification of life insurance applicants based on health factors and risk level is perceived as a kind of discrimination that is both rational and legal, i.e. actuarial discrimination. …
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".