Bibliographic record
Abstract
Abstract This article proposes a key principle and related concepts for reasoning about accounting estimates. The reasoning is consistent with a principles‐based professional judgment framework proposed by Ross Skinner and the Institute of Chartered Accountants of Scotland. The principle deals with reasonable ranges and related risk assessments in the audit of accounting estimates. It does so by using concepts first introduced by Boritz and Skinner and updates them for the requirements of CAS/ISA No. 540 and International Financial Reporting Standards. The article identifies the conditions for the existence of the benchmark ranges proposed by Smieliauskas in identifying fairly presented estimates. The need for a professional judgment framework and related guidance has been recognized recently by the International Federation of Accountants, a 2010 EU Green Paper, and the Public Company Accounting Oversight Board as a result of challenges auditors have been facing in the current reporting environment. This recognition echoes calls first made by Ross Skinner in his pioneering 1995 article, and reinforced by the FASB/IASB 2006 proposal for principles‐based accounting standards.
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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 | 0.036 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".