LEGAL REGULATION OF GENETIC RESEARCHES IN THE RUSSIAN LEGISLATION IN THE CONTEXT OF THE PROBLEM OF GENDER VERIFICATION IN SPORT
Bibliographic record
Abstract
Modern achievements of geneticists, which have made it possible to map the genes of inherited diseases, have set the legislator a rather difficult task to determine the grounds and limits of the use of relevant information in various spheres of public life, including insurance. At the same time, in some countries, the emphasis is on legislative regulation, which in the case of a Federal structure of the state can be carried out at different levels (USA, Canada), in others, more importance is attached to self-regulation (great Britain, Australia). The range of issues covered by the regulation is very diverse in both cases and may include: assessment of the possibility of referral for genetic research, which is usually excluded, access to existing results of genetic research, which may be restricted by a ban on their use; the right to collect genetic information without conducting genetic testing, which is recognized by the actual practice of analyzing family history; determining the conditions and limits for the use of genetic information, which are usually associated with obtaining the consent of the policyholder, ensuring the confidentiality of personal data and only for the purposes for which it was collected; establishing a correlation between the exercise of the right to access genetic data and the amount of insurance coverage, which may decrease if favorable data is obtained that contradicts the conclusions made on the basis of family history
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".