Bibliographic record
Abstract
Author: Alexandra Matiásková Title: Bioethics ΙΙΙ - Eugenics - history and present Form: Master Thesis Name of University: Charles University in Prague, Faculty of pharmacy in Hradec Králové Degree: Pharmacy Aim and task: The aim of this thesis was to elaborate historical development of the eugenics, its present in the focus on the assisted reproduction, the prenatal diagnosis and the preimplantation genetic diagnosis and solving this problematics in the Czech Republic, Germany, Great Britain, Israel, China, United states of the America and Canada by the background research method. Principal information: The main principles of eugenics were described by Platon before 2400 years. We can determine the period from the end of the 19th to the first half of the 20th century as the biggest boom of the eugenics when its negative ideas and manifestations were accepted in the society. The word itself is stigmatized because of the abuse of eugenic`s principles for the justify holocaust in the Nazi`s Germany. Demarcation of the eugenics is very disputed. It is described as aplicated human`s genetics with displays as prenatal diagnosis or preimplantation genetic diagnosis by some authors. The World Health Organization defined it as a coercive policy intended to further reproductive goal, against the rights,...
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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 | 0.002 | 0.004 |
| 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 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".