Bioethics at the scene of assisted human reproduction technologies
Bibliographic record
Abstract
Advances in Reproductive Technologies confront us with new scenarios and practices where this interrogation 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 related 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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".