The rise of the unregulated birth worker in Australia: the canary flees the coal mine
Bibliographic record
Abstract
Description \nThis book investigates why women choose ‘birth outside the system’ and makes connections between women’s right to choose where they birth and violations of human rights within maternity care systems. \n \nChoosing to birth at home can force women out of mainstream maternity care, despite research supporting the safety of this option for low-risk women attended by midwives. When homebirth is not supported as a birthplace option, women will defy mainstream medical advice, and if a midwife is not available choose either an unregulated care provider or birth without assistance. This book examines the circumstances and drivers behind why women nevertheless choose homebirth by bringing legal and ethical perspectives together with the latest research on high-risk homebirth (breech and twin births), free birth, birth with unregulated care providers and the oppression of midwives who support unorthodox choices. Stories from women who have pursued alternatives in Australia, Europe, Russia, the UK, the US, Canada, the Middle East and India are woven through the research. \n \nInsight and practical strategies are shared by doctors, midwives, lawyers, anthropologists, sociologists and psychologists on how to manage the tension between professional obligations and women’s right to bodily autonomy. This book, the first of its kind, is an important contribution to considerations of place of birth and human rights in childbirth.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".