THE RISE OF THE PRINCELINGS IN CHINA: CAREER ADVANTAGES AND COLLECTIVE ELITE REPRODUCTION
Bibliographic record
Abstract
Abstract How have China's princelings benefitted from their family backgrounds in their careers? This study seeks to answer the question and, in so doing, to add to the existing factionalist and meritocracy approaches to Chinese political elites. Based on biographical data of 293 princelings, quantitative analyses show that princelings have various advantages over non-princeling officials on the Central Committee. This is not simply familial advantage, however, as regression analysis finds parents’ rank and longevity do not significantly affect princelings’ career outcomes. Rather, the findings suggest that princelings benefit from membership in an affiliative status group, which differs from factions. The qualitative analysis find princelings’ status is formed and reproduced in a “collective” manner: (1) princelings’ status and early advantages originated in the state's centralized resource allocation system; (2) princelings’ education and career choices are intertwined with the state's practical and ideological goals; (3) princelings’ shared life courses strengthens their collective identity; (4) princelings’ career advantages are secured by the party-state's cadre management system. These factors combine to reproduce princelings’ elite status within the party and state, what I term “collective elite reproduction.”
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".