Bibliographic record
Abstract
In recent years, the scale of shadow banking in China expands rapidly, which has great impact on the traditional commercial banks and the national economy. This also attracts attentions of policymakers and academic researchers. This paper discusses concept of shadow banking and, summarizes the main classification, composition and risks of shadow banking. Then, the paper compares regulations of shadow banking in the United States, Britain and European Union. Finally proposes some enlightenment from the shadow banking international regulatory practice. variables such as the auditor tenure effects and auditee risk which have been found to have an inconclusive relationship with the amount of external audit fees in prior studies. However, the auditee size seems to have been the key determinant of external audit fees. Furthermore, financial risk is found to be negatively and significantly associated with the level of external audit fees. On other side, empirical results found that the audit tenure has no significant relationship with audit fees. Finally, the current study is unique because it is the first to empirically examine factors impacting the level of audit fees in Jordan for a total of three years; it revisits the audit fee literature and highlights the important determinants that affect audit fees.
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 | 0.004 | 0.009 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".