Compare the Disclosures of U.S.-listed Chinese Companies with U.S.-based Companies
Bibliographic record
Abstract
The purpose of this study is to compare the differences in disclosure quality between Chinese and American companies listed in the United States. Despite the fact that Chinese companies listed on a U.S. stock exchange are required to adhere to the same disclosure and financial reporting regulations as U.S. companies listed on that exchange, variations between the two persist. Consequently, this study seeks to explore and compare the specific disparities in disclosure quality between Chinese companies listed in the U.S. and American companies. The "use of proceeds" section of the initial IPO prospectus for both American and Chinese companies will be employed to assess the specificity of disclosure quality. Statistical data sampling and analysis will be conducted to compare their specificity of disclosure. Finally, a T-test will be employed to compare and contrast the results. Based on our research findings, it can be concluded that Chinese companies listed in the U.S. exhibit a significantly higher overall quality of information disclosure compared to domestically listed U.S. companies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".