"Sadly and with a Bitter Heart": What the Caesarean Section Meant in the Middle Ages
Bibliographic record
Abstract
One sunny spring day, a Resurrectionist priest sips tea and speaks of his time as a Bolivian missionary in the 1960s and ’70s. His recollection of the local ‘Indians’ is obscured by more than three decades’ distance. China cup in hand, he recalls vaguely their mud huts, flocks of sheep, herds of llamas, and the beautiful, rugged terrain of the altiplano. With greater precision, he speaks about the local belief system, especially attitudes towards stillbirths. This left a strong impression upon him. The priest emphasizes how deeply fearful the locals were of stillborn babies, and he flavours his recollections with two sad anecdotes. One day, he says, some villagers brought him a small blue corpse. The baby’s father insisted that the missionary baptize it. Since this was canonically impossible, the priest performed an impromptu blessing. It effectively banished the evil spirit conjured by the unfortunate birth. Satisfied with the blessing, the villagers relaxed and returned to their normal lives. On another occasion, one of the priest’s confrères was less delicate. A mother presented him with her dead baby, pleading for a postmortem baptism. At last the cleric told her, “The Church will only permit me to baptize your child if it draws milk from your breast.” Since this was impossible, the mother went away frustrated and ill at ease, having been unsuccessful in her bid to exorcise the unlucky spirit.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".