Bibliographic record
Abstract
27 assurance independence, 162 Assurance Services Executive Committee, 294 Auditing Procedures Study, 178 Committee on Continuous Auditing, 130 Elliot Committee, 129, 152, 158 Enhanced Business Reporting project, 8 Red Book, 7, 8, 16, 46, 249, 261 Statement on Auditing Procedure, 286, 288À289 Systems Reliability Task Force, 129À130 SysTrust, 130, 152, 158, 160 Vision Project 1998, 170 WebTrust, 130, 152, 158, 160 Analytical procedures, 277 Analytic method, 34À36, 78 Analytic monitoring, for continuous assurance, 191À215 automatic confirmations, 212À213 continuity equations, 208À209 control tags, 213 data taps, 211À212 dynamic reconciliation of accounts, 211 hierarchy of auditing, 204À205 levels of assurance and audit objectives, 196À202 MC layer, 205À207 outcomes, 213À214 process, 203À204 supply and demand, 193À196 tagging data accuracy, 209À210 tertiary 'black box' monitoring, 207 time series analysis, 210À211 timing of, 202À203 tools for, 207À213 Annual World Continuous Auditing and Reporting Symposium, 323 Anti-money laundering, 15 Apple, 43 Application agents, 306À308, 311 Approva, 249, 250 Archival audit, 150 Article collection, 62 Article source, 62 Artificial neural network assistant (ANNA), 61 Association of Certified Fraud Examiners, 289 Assurance, 8 continuous, 149À165, 251 continuous data, 9, 261À268, 277 control level, 30 costs of providing, 160À161 embedded modules, 163À164 entity, 36 estimation of, 200À201 independence, 162, 164 judgment, 201À202 levels of, 29À30, 196À202 measurement rule, 199À200 products, ownership of, 161À162 Assurance Services Executive Committee (ASEC), 294 Emerging Assurance Technologies Task Force, 27 Assuror's independence, and continuous assurance, 162À164 AT&T Corp., 3, 248, 273 continuity equations, 208 continuous monitoring versus continuous auditing, 18 Continuous Process Audit System.See Continuous Process Audit System (CPAS) RCAM system, 11, 17À18 transaction evaluation, 199 Attestation, 88, 92 Audit Action Sheets (AASs), 224À226, 236, 239, 254, 256, 259, 260 selection of, 230À233 AUDITAPE, 287 Audit applications approach, 294 Audit Applications Group (AAG), 184 Audit automation, 56À57, 75 Audit Command Language (ACL), 175, 195, 248, 249, 250, 268, 288 Audit Data Standard (ADS), 26À28 ecosystem architecture, 28 Audit data warehouse model, 293À294 Audit ecosystem, 42À45 characteristics of, 43, 299, 309 defined, 299 external influences on, 310 to support blockchain-based accounting and assurance, 299À312 AuditeesÀauditors relationship, 144 Audit fatigue, 21 E-Audit, 230À231, 258 EbXML, 153, 158 Economic feasibility, of continuous online auditing, 127À128 EDGAR system, 65, 172, 250 ElderTrust Plus, 152 Electronic commerce, 170 Electronic data interchange (EDI), 57, 163, 170, 250 Electronic data processing (EDP), 56À57, 126, 135, 179, 287
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.702 | 0.727 |
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".