Bibliographic record
Abstract
Before they were grandmothers women were mothers-in-law. Until recently they played a major role in the arrangement of their sons’ marriages. Young brides moved into their families, the start of one of the most toxic family relationships. They looked eagerly for signs of pregnancy. If this did not come about they prayed for one, even taking their daughters-in-law to a shrine of Guanyin, the goddess who ‘sends sons’. Son-preference was embedded. Today her shrine on Putuo Island is one of the major pilgrimage sites in China. Once a pregnancy was established the grandmother-to-be put the mother-to-be on a strict regime. The older women supervised the birth and the month-long sequestration of the new mother, and fed her special foods to encourage the flow of rich milk. The grandmother took control of the infant, tending to it ceaselessly. Babies were held constantly, and even slept with their grandmothers. The aim was to make the baby happy, placid and adorable. These traditions have weakened but not disappeared. The dominance of the mother-in-law is weaker – and paternal grandmothers coexist with maternal ones. Baby worship continues.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".