Genetic Information in the Light of Genetic Discrimination: the Experience of Foreign States
Bibliographic record
Abstract
Background. Genetic information is often used for purposes of discrimination. For example, the results of genetic testing can demonstrate a high risk of developing a disease in an apparently healthy person, which will require expensive medical care. Such information may affect the decision on the employment of a candidate for a job or the conditions for concluding an insurance contract with him/her. Objective. The article discusses major issues of legal regulation of public relations arising from protection against discrimination based on genetic status in the legislation and law enforcement practice of a number of foreign countries (Australia, Canada, the USA).Design. 20 studies written in English were retrieved from Scopus and Web of Science databases.Results. The research methodology is based on dialectical, logical, predictive methods, system analysis, content analysis, as well as private scientific methods (statistical, technical legal, comparative legal methods). The article provides an overview of the international legal framework for the regulation of public relations arising from countering discrimination based on genetic status, as well as key acts of leading foreign jurisdictions and law enforcement practice.Conclusion. In conclusion, the author reflects on the advisability of implementing relevant foreign experience into the Russian legal system.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".