Bibliographic record
Abstract
The new International Ethics Standards Board for Accountants (IESBA) standard on noncompliance with laws and regulations (NOCLAR) has been adopted by over 100 countries, including Canada and Mexico and is well on the way to becoming the global standard for accounting ethics. U.S. accounting professionals may become subject to the IESBA standard by working in a country or jurisdiction that has adopted the standard, working as part of a professional network that has adopted the standard, or performing services for a company that is part of a group governed by IESBA standards. While the IESBA standard resembles U.S. standards, it is more inclusive. U.S. standards exclude from consideration illegal acts (similar to NOCLAR) that do not have a material effect on the financial statements, while IESBA standards includes NOCLAR even when not material. Accounting professionals should recognize that they may have broader responsibilities for resolving NOCLAR when working under IESBA standards. © 2017 Wiley Periodicals, Inc.
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 | 0.040 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".