Human Rights and Assisted Reproductive Technologies (ART): A Contractarian Approach
Bibliographic record
Abstract
What are human rights? Do they exist? I propose to answer these questions by advancing a contractarian account of human rights. I focus on the human right to found a family and have children. I also show how the contractarian approach to human rights can explain the current relevance of reproductive rights in the human rights discourse, and how the emergence of ART (Assisted Reproductive Technologies) has contributed to this shift. The contractarian account of human rights asks, firstly, the following question: which basic needs and desires can be ascribed to any human being regardless of gender, nationality, sexual orientation, age, ethnicity etc.? Having an interest, for instance, in preserving one’s own bodily integrity, freedom, and private property qualifies as a basic human need or basic desire. But a basic human need or desire does not constitute in itself a human right. Secondly, the contractarian account of human rights asks, then, which basic human needs or basic desires individuals and states representatives would consider so important that they would agree to create institutional frameworks, both at the domestic and international level, in such a way as to enable individuals to pursue the fulfilment of their basic needs or desires without state interference. Human rights exist and can only be claimed in the context of these normative frameworks.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.082 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".