Bibliographic record
Abstract
This paper anaylaized a Chinese local gazztter of Yiyang county in Guangxin prefecture in the west of Jiangxi province. Is't edditor Tan Xuan accomplished a various administrative achievement with moralirty. We scoped on the Tan Xuan's discourse in this gazztter for stoping two customs when he was magistrate of Yiyang county. In addition, through a demographic perspective for growing total popuration and changing the ratio of male and female from the 16 th century to the 19th century, we considered historically a characteristic of Tan Xuan's discourse. There were two bad customs to decide a fate of women and chilldren in Premodern China. One is "ni nu" corresponds to infanticide, but the word to be interpretd literally that force a new born daughter to death by drowing, and other "mai qi," "yu qi," "dian qi" or "zu qi " is meaning that a husband compel his wife to live in othter man's house as stopgap wife without divorce like a lending land. Tan Xuan wrote two essays to pointout its customs bring about such a social problem as low nuptiality and unstable marrage life, and to advise the inhabitants to abandon it, while he made an effort to revive the region severely devastated by rebellions and natual calamities. His essays was inherited in the future period over. However, the femal popuration was less than that of male as before. This tendency was the same as all over China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".