Bibliographic record
Abstract
Accounting. see also Financial accounting choice, 325 corporate fraud case, 158-160 jobs, 262-263 Accounting and Auditing Enforcement Releases (AAER), 89 Accounting error earnings management and fraud, 325-327 limits of measurement technology, 323 management bias, 324-325 randomness, 323-324 sources of, 322-327 Accounting fraud, 25 capital market incentives, 328-330 contracting incentives, 327-328 managers committing, 327-330 Accounting principles, 63-78 conservatism principle, 71-73 corporate reporting fraud, 61 cost principle, 73-75 economic entity principle, 67-71 full disclosure principle, 65 going concern principle, 65 matching principle, 77 materiality principle, 65-67 monetary unit principle, 64-65 revenue recognition principle, 75-76 time period principle, 78 Accounting scandals.see also Global corporate scandals, 345-358 Enron, 345-351 future regulation, 358 Global Crossing, 355-358 WorldCom, 352-355 Accounting Standards Update (ASU), 66 Accrual-based earnings management, 118-119 Advanced persistent threat, 289 Adverse selection, 152-153 Affiliate fraud, 288 Agency costs, 156-158 bonding costs, 157 monitoring costs, 156-157 residual losses, 158 Agency relation, 150 Agency theory, 10, 150-158, 170-172, 453 agency costs, 156-158 fundamental role of information asymmetry, 151-154 interest divergence and opportunistic behavior, 154-155 Parmalat fraud, 160-163 Xerox case, 158-160 Alabama's Data Breach Notification Act (2018), 294 Alberta, whistleblowing programs in, 254-255 Alberta's Securities Commission (ASC), 254 American Depositary Shares (ADS), 404 Amsterdam Stock Exchange, 403 Anstalts as shells, 366-367 Anticorruption, 198 Antimoney laundering (AML), 374-375 Asset forfeiture, 312-313 misappropriation, 7-8, 24-25, 28-29, 321 Asset protection trusts, 367 as shells, 367-368 Assistant US Attorneys (AUSAs), 302, 304 Association of Certified Fraud Examiners (ACFE), 3, 22-23, 39, 107, 321-322 At-the-money stock options, 385 Attorney attorney-client privilege, 263-264 work product, 263-264 Audit committee financial expert, 113-114 functions, 112-114 Cash, 274-275 cash-for-information system, 242 misappropriations, 28 Certified Fraud Examiners (CFEs), 262-263 Channel stuffing, 33-34, 270, 326 Chief executive officer (CEO), 19, 86, 108, 155, 169, 172, 187, 213-214, 344, 383, 406-407, 428, 444 CEO-chair duality, 115 narcissism, 97-99 power, 99-100 Chief financial officers (CFOs), 94, 179, 352, 409, 444 China Securities Regulatory Commission (CSRC), 139-140 Chip-and-PIN, 287 Citibank, 410 Civil cause of action.see Private cause of action Clean fraud, 286-287 Cockroach theory, 3 Code of Conduct, 275 Cognitive dissonance theory, 43 Cognitive theory of moral disengagement, 43-44 Committee of Sponsoring Organizations of Treadway Commission (COSO), 170 Common Reporting Standard (CRS), 375 Company foundation (CF), 370 as shells, 370 Compensation contracts, 329 and fraud, 175-176 Concealed liabilities and expenses, 34 Conflict of interest, 31 Conservatism principle, 71-73 Consumption taxes, 364 Contagion effect.see Spillover effect Contracting incentives, 327-328 Contributor level fight against fraud, 232-233 Control mechanisms, 86 Cookie jar reserve, 324 Cooperative Capital Markets Regulatory System, 255 Copyright, 288
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.732 | 0.802 |
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".