Bibliographic record
Abstract
If we take a look at the legal and monitoring systems and cases regarding prevention of listed companies from making misstatements in economies such as those of the U.S., the U.K., Germany, Japan, mainland China, Hong Kong and Taiwan, what we can find in common are the following elements which are closely associated with the making of misstatements: Related parties listed companies and their staff (directors of the board, supervisors, financial and accounting staff); the stock exchange, the securities brokers’ association; the stock exchange regulatory authorities (the Stock Exchange Regulatory Committee or the Bureau of Financial Administration); judicial organs (law courts, prosecutorial authorities) and investigation authorities for securities crime (the public security department or the securities investigation bureau) and their staff; intermediary institutions (accounting firms, investment banks, securities consulting and analysis firms, and law firms) and their staff; investors (including institutional and individual investors). The legal system and regulatory system: relevant corporate/company laws (including accounting rules) that dictate the substantial framework of a company’s operations; and procedural law, domestic law, international cooperation agreements, and the laws and regulations of every country, provide rules for all the above-mentioned parties. Despite the presence of such laws, rules and regulations, companies still manage to maneuver round them, taking advantage of some of the (sometimes illegal) loopholes and “playing the game” for immediate gain in the hope that they will never be caught. Technical equipment, which includes electronic exchange systems, transaction surveillance appliances, and so on. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".