The innovative personality? Policy making and experimentation in an authoritarian bureaucracy
Bibliographic record
Abstract
Summary Why do local officials in an authoritarian bureaucracy experiment with policy, even when directed not to do so by central‐level officials? This study suggests that policy experimentation in this institutional environment can best be understood as an interaction between the structure in which local officials are embedded and individual‐level personality attributes. Leveraging a new data set from a series of original surveys with local policy makers in mainland China, conducted between 2016 and 2018, we discern three baseline personality types: authoritarian, consultative, and entrepreneurial. We thereafter examine the individual‐level characteristics of local officials who will innovate irrespective of a centralization of bureaucratic power and interests, as currently experienced under Chinese President Xi Jinping. We find that local policy makers engage in policy innovation when they are more focused on resolving governance problems and that increased risk reduces but does not eliminate their willingness to innovate. Based on these findings, we contend that future studies of policy innovation should use an evolutionary framework to examine the interaction between preferences and selection pressures.
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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 | 0.006 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".