Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it