Divulgation de l’information génétique en assurances
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
Today, medical innovations arising from genetic research include the ability to predict, using genetic testing, the future health of certain individuals in particular as to their risk of developing certain diseases such as breast cancer. These advances have generated several therapeutic benefits but also entail new challenges for individuals. Indeed, genetic results generated may raise additional issues related to the use of this information outside of the therapeutic or medical research contexts. Many third parties such as insurers and employers have shown interest in using this information. In the insurance context, such use is likely to lead to a differential treatment of individuals based on their genetic characteristics at the time of purchase of personal insurance, potentially giving rise to the phenomenon of genetic discrimination. Unlike other jurisdictions, the law in Quebec does not provide specific rules on the use of genetic information. This status quo raises several issues in the context of insurance law. What is the scope of the duty to disclose of an insurance applicant and an insured concerning his genetic risks? What is the role of the insurer in the assessment of genetic risks? The study of various issues related to the possible use of genetic information in personal insurance and the duties of the applicant, the insured and the insurer upon subscription or renewal of an insurance policy reveals several uncertainties that may eventually require further clarifications from the legislator or the courts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| 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) | 0.001 | 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 it