Bibliographic record
Abstract
Death has traditionally been regarded in China as something to be prepared for, not as something to be feared, a taboo subject. As age came on grandmothers prepared for their end. If the family did not have a graveyard they arranged a grave site. They had a coffin made, of the most expensive wood they could afford. They ordered a set of grave clothes. The set aside money for the funeral. The division of property was done by customs; wills were not legal documents but moral exhortations to descedents. In the Mao Era most of these practices were considered feudal and outlawed, in favour of cremation without ceremony. In the Reform Era many have come back, though cremation is encouraged. The dead live on. In the past they joined the ancestors. Now the focus is on commemorating individuals. At the Qingming Festival families remember the dead and provide them with paper replicas of what they may need in the afterlife. In a breach with tradition, neither of China’s twentieth-century leaders has been buried. Mao Zedong lies in the centre of Tiananmen Square. Chiang Kai-shek is in a coffin in Taoyuan (Taiwan), waiting to be buried in his home town.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.019 |
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".