A PROTEÇÃO DOS DADOS GENÉTICOS E O DIREITO CONSTITUCIONALÁ PRIVACIDADE
Bibliographic record
Abstract
RESUMO Este artigo objetiva analisar como o acesso as informações genéticas pode afetar diretamente o direito à privacidade, através de análise em leis e documentos que proíbem o acesso a tais informações. Tal informação deve ser feita de forma cautelosa e sigilosa, o seu uso demasiado poderá acarretar danos irreparáveis a pessoas avaliadas, e como consequência surgir à discriminação.O direito à privacidade abarcado no art. 5º do nosso texto Constitucional possui um caráter positivo dando ao indivíduo o controle de suas informações pessoais, podendo não somente impedir a sua utilização, como também definir quais as informações poderão ser utilizadas.Palavras-chave: Dados Genéticos; Direito à Privacidade; Discriminação; Informações Genéticas; Proteção.ACCESS TO WORKER'S GENETIC INFORMATION AND CONSTITUTIONAL LAW PRIVACY ABSTRACTThis article aims to analyze how access to genetic information can directly affect the right to privacy, through analysis of laws and documents that prohibit access to such information. Such information must be made in a cautious and confidential manner, its use too much may cause irreparable damage to people evaluated, and as a consequence, discrimination may arise.The right to privacy covered in art. 5 of our Constitutional text, it has a positive character giving the individual control of his personal information, which can not only prevent its use, but also define which information can be used.Keywords: Genetic data. Right to Privacy; Discrimination; Genetic Information; Protection
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".