La bioética en el escenario de las tecnologías de reproducción humana asistida
Bibliographic record
Abstract
Summary Advances in Reproductive Technologies confront us with new scenarios and practices where this interro‐ gation seems to be necessary. Throughout the cinematic narrative presented in the film Starbuck or the remake Delivery man (Ken Scott, Canada, 2011, USA, 2013) we can analyze different perspectives relat‐ ed to Artificial Insemination with Donor, in dialogue with the right to identity and the right to privacy. The story of the main character of the film (David) tells two events that change the course of his life: first, his girlfriend reveals that she is pregnant and plans to have the child without his help, and that same day he was told that there’s a claim about David’s sperm donation, around twenty years ago. As a result of these donations, 533 children were born, of whom 142 want to know him, find out his name. This unusual situation leads us to analyze it from three perspectives: from the bioethical point of view, from the responsibility and subjectivity.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| 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; both teacher heads agree on what is shown here.
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".