(Not) wanting to choose: outside agencies at work in assisted reproductive technology
Bibliographic record
Abstract
Human choice and interventions that could seem to threaten the course of 'nature' or 'chance' are at the heart of controversies over assisted reproductive technology across Western countries. These debates focus predominately on so-called 'selective reproductive technology'. While today, the technique of in-vitro fertilization (IVF) raises few political and bioethical debates in France and other Western countries, concerns remain that human intervention might replace 'natural' processes, threatening human procreation. These polemics focus on situations that require a decision, notably embryo selection and the fate of spare frozen embryos. The choices involved are induced by the technology and organized by the law. In the French legal system, IVF patients and professionals have the opportunity and, to a certain extent, the responsibility to decide on the status of in-vitro embryos. This article shows that, in these situations, both IVF patients and professionals invoke outside agencies ('instances tierces'), both to avoid making decisions and to recover a world order in which procreation is not entirely subject to human decision. In short, there is a need to feel that procreation is not entirely dependent on human intervention; that individuals do not decide everything. It appears that the choices that are made, their nature and the type of outside agency that is invoked are highly situated.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".