The Panorama of CEO Turnover: an Empirical Study on Chinese Listed Companies
Bibliographic record
Abstract
In this paper, we present a panorama of the Chinese listed companies’ CEO turnover. We can see that from 2001 to 2004, the top three industries about the number of CEO turnover are Manufacturing, Mixed and Telecommunications industries; the top three industries about relative turnover rate are Media and culture, Telecommunications and Mixed industries; job re-arrangement, resignation and expiry of tenure are three mainly publish reasons; the difference between inside and outside succession is not significant. A deep analysis showed that age, education and tenure will influence CEO turnover too. Key words: Chinese listed company, CEO turnover, inside succession, outside succession Resume: Dans le present article, nous presentons un panorama du roulement de CEO des societes cotees chinoises. Nous constatons que, de 2001 a 2004, les trois top industries sur le nombre du roulement de CEO sont l’industrie de fabrication, l’industrie mixte et les telecommunications. Les trois top industries sur le taux de roulement relatif sont les medias et culture, les telecommunications et l’industrie mixte. Le changement de travail, la demission et l’expiration du mandat sont les trois raisons essentielles. La difference entre la succession interne et la succession externe n’est pas importante. Une analyse profonde indique que l’âge, l’education et la duree de mandat peuvent aussi influencer le roulement de CEO. Mots-Cles: societe cotee chinoise, roulement de CEO, succession interne, succession externe
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".