Analyzing the Personality of Party and Government Leaders: A LDA Topic Model
Bibliographic record
Abstract
Purpose/Significance: Personalities of party and government leaders play a pivotal role in shaping their preferences, attitudes, and conduct in the context of strategic decision-making. The analysis of personality carries substantial implications for the assessment and selection of leadership cadres. The study endeavors to introduce an innovative method for scrutinizing the personalities of party and government leaders, leveraging the technological topics generated through a Latent Dirichlet Allocation (LDA) model.Methods/Procedures: Anchored in the theory of personality behavior, the study posits that discernible topics can be extracted from news reports that document the political activities of party and government leaders. These discerned topics can be employed to make inferences about their personalities. Consequently, we systematically amassed news reports chronicling the political engagements of 62 party and government leaders spanning 31 provinces, municipalities, and autonomous regions in mainland China, covering their tenures in office up to 2021. Based on the LDA model, we conducted an analysis to uncover the dimensional structure of their personalities, incorporating techniques such as topic clustering and common factor extraction.Results/Conclusions: The study effectively identified six distinct personalities that correspond with Hollander's views on occupational personality. The development gave rise to a theory that specifically addresses the personalities exhibited by party and government leaders. Spatial analysis validated the presence of a spatial aggregation effect, underscoring the validity of our framework. Our study can provide implications for the training, evaluation, and designation of party and government leaders in the future.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".