Moving the Conceptual Framework Forward: Accounting for Uncertainty
Bibliographic record
Abstract
ABSTRACT To meet the objectives of financial reporting in the IASB's Conceptual Framework, the “balance‐sheet approach” embraced by the Framework is necessary but not sufficient. Critical, but largely overlooked, is the role of uncertainty, which we argue defines the role of accrual accounting as a distinctive source of information for investors when investment outcomes are uncertain. This role is in some sense paradoxical: on the one hand, uncertainty undermines both the balance sheet (because uncertain assets are unrecognized) and the income statement (because mismatching is unavoidable). However, these inevitable accounting effects can be exploited to provide information about uncertainty, though not by a balance‐sheet approach alone. Rather, balance sheet recognition and measurement criteria are established by consideration of the impact of uncertainty on matching and mismatching in the income statement. This combination of balance‐sheet and income‐statement approaches enhances the communication of information to investors under conditions of uncertainty, thereby giving greater clarity and purpose in satisfying the objective of the Framework to provide information about “the amount, timing, and uncertainty of future cash flows.”
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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 | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".