Kirsty Horsey (ed.), Revisiting the Regulation of Human Fertilisation and Embryology
Bibliographic record
Abstract
In this edited collection, Kirsty Horsey brings together a range of experts in law and ethics who offer a critical evaluation of recent developments relating to the UK’s laws on human-assisted conception. Despite the title’s use of the term revisiting, the contributors have, in fact, focused on current topics in the field rather than a general re-exploration of the chronological development of regulation in human fertilisation and embryology. Horsey demonstrates the overarching purpose of the book by asking, in the preface, ‘whether the Human Fertilisation and Embryology Act 2008 has achieved the stated aim of being “fit for purpose” or, if not, what could or should be done to improve it’ (p x) . Correspondingly, what follows in the next thirteen chapters is an examination of the law and ethics of human fertilisation and embryology with topics including changing conceptualisations of child welfare and the parenting provisions,1 problems with DIY-assisted conception,2 prisoners’ access to fertility services,3 attribution of legal parenthood in surrogacy,4 mitochondrial DNA transfer,5 as well as chapters which offer a comparative perspective on the UK’s regulation with that in Canada and Australia.6
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".