Bibliographic record
Abstract
The rapid rise of Chinese companies in the global economy has attracted great scholarly attention to Chinese corporate governance. Among the various areas of Chinese corporate governance, executive compensation is an important yet difficult part to research. The common research method of Chinese executive pay literature relies on pay figures disclosed in listed companies’ annual reports and tends to take the disclosed numbers at face value. This Article discusses three informal pay practices that constrain the usefulness and reliability of executive pay data formally disclosed in annual reports of Chinese listed companies, especially those owned by the state. A valid reading of formal pay figures entails an understanding of the network structure and the political environment in which Chinese companies operate. An investigation of the practices behind formal compensation numbers sheds light on many issues for scholars and policymakers, the salience of which escalates as the international interaction with Chinese companies expands. For example, it stresses the important role of political institutions in shaping executive compensation; it raises questions about the extent to which international cross-listing improves transparency of Chinese companies; it critically evaluates whether China’s latest reform policy deals with the real problems of its state-owned enterprises; it spotlights the lacuna of extant scholarship on Chinese executive compensation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".