The innovative personality? Policy making and experimentation in an authoritarian bureaucracy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it