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Bibliographic record
Abstract
With the onset of the Human Genome Project, social concerns about �쁤enetic information discrimination�� have been raised, but the problem has not yet been highlighted in Korea. However, non-medical institutions�� genetic testing which is related to disease prevention could be partially allowed under the revised �쏝ioethics and Safety Act�� from June 30, 2016. In the case of one domestic insurance company, DTC genetic testing was provided for the new customer of cancer insurance as a complimentary service, which made the social changes related to the recognition of the genetic testing. At a time when precision medicine is becoming a new standard for medical care, discipline on genetic information discrimination has become a problem that can not be delayed anymore. Article 46 and 67 of the Bioethics Act stipulate the prohibition of discrimination on grounds of genetic information and penalties for its violation. However, these broad principles alone can not solve the problems in specific genetic information utilization areas such as insurance and employment. The United States, Canada, the United Kingdom, and Germany have different regulations that prohibit genetic information based discrimination. In the United States, Genetic Information Non-Discrimination Act takes a form that adds to the existing law about the prohibition of genetic information discrimination. In addition, the range of genetic information includes the results of genetic tests of individuals and their families, including �쐄amily history��. Canada has recently enacted legislation in 2017, expanding coverage to general transactions of goods or services in addition to insurance and employment. The United Kingdom deals only with �쁯redictive genetic testing results of individuals��. In the case of insurance, the UK government and Association of British Insurers (ABI) agree to abide by a policy framework (�쁂oncordat��) for cooperation that provides that insurers�� use of genetic information is transparent, fair and subject to regular reviews; and remain committed to the voluntary Moratorium on insurers�� use of predictive genetic test results until 1 November 2019, and a review of the Concordat in 2016. In the case of employment, The ICO�셲 �쁄mployment Practices Code (2011)�� is used as a guideline. In Germany, Human Genetic Examination Act(Gesetz 체ber genetische Untersuchungen bei Menschen) stipulates a principle ban on the demand for genetic testing and the submission of results in employment and insurance. The evaluation of the effectiveness of regulatory framework, as well as the form and scope of the discipline is different from country to country. In light of this, it would be desirable for the issue of genetic information discrimination in Korea to be addressed based on the review of related regulations, the participation of experts, and the cooperation of stakeholders.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.025 |
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".