Fit or fitting in: deciding against normal when reproducing the future
Bibliographic record
Abstract
‘Normal’ is a contentious term. Descriptively, ‘normal’ represents ‘what is’ as a statistical average. However, the term also represents normative or prescriptive content about what is ‘right’ or ‘what should be’. Correspondingly, abnormality is a deviation from the norm. It is both a factual exception to the average and a value judgement about what is a ‘wrong’ state of being. Pursuing ‘normal’ or deciding against it can be a defining moment in the high technology environment of assisted reproduction. Here, we explore notions of normalcy articulated through legal and policy regimes around screening and testing of gamete and embryo donors. We draw on the work of disability scholars and the diversity of responses to the idea of normal that were registered by four women interviewed in our studies. Three of the interviewees had used or were intending to use donated gametes and the fourth had intended to donate her embryos. We demonstrate how the choice of a particular donor may reveal ingrained or structural prejudice that reconstructs difference as disability. Equally, however, it may reveal a multitude of ways in which difference or deviation from a normative standard is incorporated as a normal part of family formation.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".