The Relationship Between Political Leader’s Personalities and Regional Social Governance Modernization
Bibliographic record
Abstract
The study aims to investigate the relationship between the personalities exhibited by party and government leaders and the modernization of regional social governance. It aspires to offer novel insights to empower the local governments of Chinese provinces, thereby assisting them in augmenting the modernization of social governance.Led by upper echelons theory, we proposed an analytical framework to explore the relationship. We firstly collected the texts of the activity reports of the leaders in 31 provinces of mainland China during the years 2018 to 2020. Their personalities were discerned through the application of Latent Dirichlet Allocation (LDA) topic modeling and principal component analysis. Integrating the dimensions of processes and outcomes, we then devised an evaluation system to assess the level of regional social governance modernization. The system underwent a comprehensive evaluation employing principal component analysis. Finally, we employed regression analysis and the Quadratic Assignment Procedure (QAP) regression analysis to quantitatively assess the effect of political leader’s personalities on the modernization level of regional social governance.According to our results, the personalities exhibited by political leaders could be classified into six dimensions, aligning with the framework proposed by Holland vocational interest theory. We revealed that the six types of personalities indeed exert influence on the modernization of regional social governance. However, compared to the effect of environmental factors such as economic conditions and the availability of skilled personnel, their explanatory power is relatively weaker. Among the identified personalities, the leaders displaying realistic and research-oriented personalities demonstrate a heightened capacity to yield substantial positive impacts on social governance modernization. Additionally, social personality also plays a discernible role in the context. Furthermore, the amalgamation of political leaders’ personalities illuminates a nuanced picture marked by moderate influence and intricate complexity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".