CEO Diversity, Political Influences, and CEO Turnover in Unstable Environments: The Romanian Case
Bibliographic record
Abstract
This work expands the literature on a less studied topic, the Chief Executive Officer (CEO) turnover in post-communist economies, analyzed during an unstable and ambiguous economic and financial environment. For the period 2005–2010, the results indicate the political inference in CEO turnover decision for the Romanian listed companies. In this period, with great turmoil in the economy determined by the financial crisis of 2008, we also find that CEO gender helps to explain the probability of changing the CEO. Moreover, this paper empirically tests if the financial and corporate governance determinants that are validated in the existing literature work for the Romanian listed companies. We reinforce that CEO turnover decision is negatively related to accounting-based performance. We find evidence of the “voting with their feet” behavior of institutional investors, and of the lack of Board of Directors monitoring. The CEO–Chairman duality and the controlling power of the largest shareholder act as entrenchment mechanisms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".