The Elastic Ceiling: Gender and Professional Career in Chinese Courts
Bibliographic record
Abstract
Since the 1990s, the number of women in Chinese courts has been increasing steadily. Many women judges have risen to mid-level leadership positions, such as division chiefs and vice-chiefs, in the judicial bureaucracy. However, it remains difficult for women to be promoted to high-level leadership positions, such as vice-presidents and presidents. What explains the stratified patterns of career mobility for women in Chinese courts? In this article, we argue that two social processes are at work in shaping the structural patterns of gender inequality: dual-track promotion and reverse attrition. Dual-track promotion is dominated by a masculine and corrupt judicial culture on the political track that prevents women from obtaining high-level promotions, but still allows them to rise to mid-level leadership positions on the professional track based on their expertise and work performance. Reverse attrition enables women to take vacant mid-level positions left by men who exit the judiciary to pursue other careers. Taken together, the vertical and horizontal mobility of judges in their career development presents a processual logic to gender inequality and shapes women's structural positions in Chinese courts, a phenomenon that we term the “elastic ceiling.”
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".