Bibliographic record
Abstract
This book examines aspects of English, United States, Canadian, and Australian law as it applies to the fetus, the pregnant woman, and the doctor or midwife who provides antenatal and perinatal care. A major part of the book consists of a critical examination of the law’s attempts to protect the fetus, which is threatened by conduct such as a criminal assault, maternal drug-taking, or parental refusal of medical treatment. The remainder of the work deals with the rapidly expanding and increasingly complex body of law on the liability of obstetricians, general practitioners, and midwives when negligent antenatal or perinatal care has been provided. There are numerous situations in which these health care professionals (or their employees) can be liable to pay damages. They can be sued if their negligence has harmed the mother, caused the birth of an injured, disabled, or stillborn child, or has resulted in the birth of an unwanted child. The book provides a comprehensive statement of the law on antenatal intervention to protect the fetus and on the responsibilities of those who care for pregnant women. By examining the nature of the relationship between a pregnant woman and her fetus, and the operation of child protection and criminal laws and the law of negligence, the book explores questions about maternal autonomy, the rights of the woman and the fetus, and the role of the law in protecting those rights and providing compensation when something goes wrong.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".