Bibliographic record
Abstract
Imagine for a moment a series of disasters that kills more than 800 women every day for a year: the equivalent of two or three daily crashes of crowded long-distance airliners or the equivalent of the direct death toll from the attack on the World Trade Center and the Pentagon every four days. There is little question that such a situation would quickly be regarded as a humanitarian emergency, as the stuff of headlines, especially if ways of preventing the events were well known and widely practiced (as is the case with avoiding crashes in civil aviation). However, remarkably little attention is paid outside the global health and human rights domains to complications of pregnancy and childbirth that kill more than 300,000 women every year – a cause of death now almost unheard of in high-income countries (HICs), although this would not have been the case a century ago. A Canadian woman's lifetime risk of dying from complications of pregnancy or childbirth is 1 in 8,800; for a woman in Sub-Saharan Africa, the world's poorest region, it is 1 in 36 (Maternal Mortality Estimation Inter-Agency Group, 2015).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".