Bibliographic record
Abstract
Grandmothers found/find many ways of making old age enjoyable. They grow scented, flowering plants, indoors and outdoors. They sew and knit. Shopping tests their wits – in the past dealing with street hawkers and market stalls, now online. Home entertainment included playing musical instruments and games of skill, chief amongst them mahjong. Entertainment has expended dramatically in the Reform Era, in the home (television, streaming) and outside; public parks have become places for the ederly to dance, sing and play games. Gossip was once a mainstay of the life of old women, within the home and in the neighbourhood. In the Mao Era old women were enlisted to watch out for politically incorrect behaviour and to enforce new rules. The advent of modern communications has reduced in-person gossip, but it still has uses, not least in the search for suitable matches for grandchildren. In the Reform Era the horizons of old people have expanded. They can travel, embark on new careers; those widowed can remarry. Their grandchildren remain the centre of their lives, even if some of the behaviour of modern youth is incomprehensible.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".