Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SYNOPSIS The controversy over Chinese reverse mergers has led to concerns about the audit quality of all U.S.-listed Chinese companies. Because a sizeable number of foreign firms cross-list their shares as American Depositary Receipts (ADRs) issued by U.S. depositary banks (as opposed to direct listings), we study how auditors have managed their audits of Chinese ADRs. Our motivation for examining Chinese ADRs is based on the findings that cross-listing via the ADR process is beneficial for U.S. shareholders. We find that relative to ADRs from countries other than China, and relative to directly listed Chinese companies, Chinese ADRs are more likely to be associated with a Big 4 auditor and are less likely to restate prior-period financial statements. We also find that Chinese ADRs pay significantly higher fees than other emerging market ADRs and Chinese direct-listings. Collectively, these results suggest high audit quality for Chinese ADRs, which is in sharp contrast to the Chinese direct-listing results. Using Tobin's Q as a measure of market value, we find that the stock market rewards Chinese ADRs, indicating that investors incorporate the benefits of higher audit quality when evaluating Chinese ADRs.
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.
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 | 0.002 | 0.021 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it