Consideração Ética sobre a Edição Genética em Seres Humanos
Bibliographic record
Abstract
A edição genética em seres humanos incita várias reflexões sobre qual deve ser o papel que a ciência deve desempenhar e quais os seus limites de atuação. Frente a essa problemática, esse ensaio teórico objetiva fornecer argumentos sobre as graves implicações éticas que tais práticas podem resultar. Como principal ponto argumentativo, será levado em conta um dos princípios éticos superiores apontado pela Ciência Logosófica: a tolerância. Nesse sentido, considerando esse princípio ético, é argumentado o por que as Ciências Biológicas não devem se ocupar com questões relacionadas a alteração dos genes. Para isso, são apresentados pontos de vista de autores a favor, e então, contrasto com outros fundamentos. A conclusão é a de que, dado os riscos e as incertezas com esses procedimentos, é mais prudente a ciência não se ocupar com essa prática.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".