Elevating Financial Reporting Beyond a Mere Compliance Instrument<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT We highlight how complying with detailed professional accounting rules can lead to lies and undermine the overall objectives of financial reporting. We do so by conducting a retrospective analysis (using the argumentation concepts of premise, qualifier, data, and conclusions) of the reasoning of the two expert accounting witnesses in the criminal trial in 2006 of Enron's president (Lay) and CEO (Skilling). We make two recommendations that are intended to elevate financial reporting beyond a mere compliance instrument and to help it conform better with the idea of fair presentation. First, we call for the objectives of financial reporting to be installed as the overarching premise in a conceptual framework (CF) for GAAP‐based financial reporting. The remainder of the CF should provide essential guidance to meet those objectives. Second, we recommend that the needs of users of financial reports should be prioritized by developing ethical forecasting principles and better ways of assessing and reporting the accounting estimation uncertainties that are identified in the IASB's (2018) revised CF. We contend that a CF with the more detailed standards should guide professional judgment on the concept of accounting risk and provide guidance on an acceptable level of this risk for various line items on the financial statements. Ideally, accounting risk should be less than 0.50 so that the probability that the reported number is materially accurate is at least 0.50. This requirement is necessary for the various types of accounting estimates to be sufficiently truthful for ethical financial reporting. This will render GAAP and financial reporting more reliant on cogent reasoning and argumentation.
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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 | 0.125 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".