Bibliographic record
Abstract
This article investigates how political categories of class influenced mate selection and marriage practices in rural China in the Mao era. Based on qualitative and quantitative data collected in thirty villages in three counties in Hebei in summer 2005, it examines class endogamy/heterogamy; class, patriarchy, and gender; and class differentials in marriage practices. The main findings include the following: (a) Though marriages were formed predominantly within the same class category, cross-class marriages did occur, but marriages between opposite class categories were less likely during the Cultural Revolution than during the pre– and post–Cultural Revolution periods. (b) Women did not invariably marry up or within the class categories under the context of the class hierarchy and patrilineal inheritance of class labels. Women were likely to marry down the political ladder when they gained economically from marriage, or when they achieved some freedom and independence within the family sphere by not living with in-laws upon marriage. (c) Sons, not daughters, of landlords or rich peasants, if they got married, did so at an older age, with larger spousal age gaps; middle and upper middle peasants could better finance their children’s marriages in terms of bride prices and dowries; and the children of landlords and rich peasants did not tend to marry someone from a long distance away.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".