Data of CEO power, chair-CEO age dissimilarity and pay gap of Chinese listed firms
Bibliographic record
Abstract
This data describes the raw and processed information such as salary, power, and age of the CEO and the chairman between 2009 and 2018 in China's listed firms. The data set contains the data of variables based on the characteristic of the firm, personal, team, and supervision. The dissimilarities and similarities of the characteristics between the chairman and the CEO are the core of this data set. The dissimilarities refer to individual and team differences. Individual differences refer to differences in age, gender, tenure, experience, shareholding, and salary of the chairman and CEO, while team differences refer to differences in team size and the standard deviation of the management board members' age. The similarities refer to joint tenure and family relations between the chairman and CEO. These variables can be used to estimate the impact of chair-CEO age dissimilarity on the relationship between CEO power and chair-CEO pay gap of the Chinese listed firms through binary probit or multinomial regression.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".