Bibliographic record
Abstract
With rapid economic growth, China has undergone substantial social, cultural and ideological transformations over the recent decades. In the meantime, trends in China’s family structure have changed dramatically as well. However, due to data limitations, research on trends in divorce has been very rare in China; especially the quantitative studies at the macrolevel. The literature indicates that despite the very low divorce rate from the 1960s to the 1970s, China’s divorce rate has increased greatly in recent decades, but this increase has been uneven in both space and time. Therefore, this paper analyzes trends in China’s divorce rate at the national, regional and provincial levels. The research results suggest that China’s divorce rate has witnessed a steady and noticeable increase in the recent two decades, with the Crude Divorce Rate (CDR) increasing by 178% and Refined Divorce Rate (RDR) increasing by 211%. Among the four provincial-level municipalities, Chongqing shows strikingly high divorce rates, whereas the divorce rates of Beijing and Shanghai have leveled off in recent years. Among all the provincial level units, the Moslem-majority Xinjiang Uygur Autonomous Region ranks first, whereas Tibet ranks last.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".