Institutional Survival under Extreme State Repression and Subsequent Revival
Bibliographic record
Abstract
This study examines institutional survival under conditions of extreme state repression.We argue that institutional values under these conditions become dormant in small "safe" social spaces such as families and small close-knit social groups.As state repression becomes increasingly violent, the suppressed groups within those spaces become more resilient in preserving "deviant" values and mitigating the negative long-term impact of state violence on institutional revival.We examine the extent to which pre-1949 entrepreneurial families served as institutional carriers for private entrepreneurship in the Mao era of China, especially in the context of the political violence of the Cultural Revolution (1966)(1967)(1968)(1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976), and shaped individuals' entry into private entrepreneurship in the post-1978 reform era.We find that entrepreneurial transmission was suppressed at the family level by communist repression.Where more severe political violence occurred, pre-1949 entrepreneurial families could better mitigate the deterrent effect on institutional revival of the number of deaths that occurred locally during the Cultural Revolution.Stigmatized pre-1949 entrepreneurial families-those with "bad" class origins-mitigated the effects better than their nonstigmatized counterparts.We test to control for public sector job opportunities at the individual and municipal levels and find that these opportunities are unlikely to drive our results.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".