Bibliographic record
Abstract
Purpose The purpose of this paper is to assess the government efforts in criminalising and combating bank fraud and corruption in China and their policy implications. Design/methodology/approach An integrated method is used to gather the data for this study, including government documents, statutes, congressional reports, legal cases, news reports, online survey and interviews with key policy‐makers, investigators and prosecutors. Findings This research finds that a major problem of bank fraud and corruption in China is the gigantic web of government officials, bank insiders and criminal businesses in committing fraud. The harshness of the Chinese law has not automatically resulted in making the struggle against bank fraud more effective. Law, enforcement and punishment are not certain, predictable, and applied consistently in order to deter fraud. Political, ideological and legal differences have hindered China's pursuit of escaped criminals in foreign countries. Practical implications This paper indicates that a three‐pronged approach – deterrence, prevention and education – is needed to address bank fraud and corruption. The industry's preventive efforts are of far greater importance than any extreme penalty. There is a need for a reconstruction of business ethics to ensure willing compliance with the law by individuals and organizations. Originality/value The paper is of value to law enforcement policy‐makers, banking regulators, financial institutions and academic researchers with interests in bank fraud and corruption issues.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".