Professional Judgment and Departures from GAAP: “Judgment in Jeopardy” Revisited*/ JUGEMENT PROFESSIONNEL ET DÉROGATION AUX PCGR: LA PIERRE ANGULAIRE REVISITÉE
Bibliographic record
Abstract
ABSTRACT Prior to 2003, the CICA Handbook both required and allowed reporting entities to depart from generally accepted accounting principles if, in the professional judgment of the preparers and the auditors, compliance with GAAP would result in misleading financial statements. In 2003, the CICA Handbook was amended to remove these provisions. In this paper, the history of the amendment is discussed in light of Skinner's 1995 article “Judg‐ment in Jeopardy". I argue that while there is evidence of certain shortcomings in the exercise of professional judgment, remedies are available. Those remedies include (1) revisiting pre‐existing recommendations; (2) improving dialogue between standard‐setters, researchers, and practitioners; (3) increasing emphasis on accounting theory in professional accounting curricula; and (4) rigorously investigating and disciplining lapses in professional judgment. I suggest that we can rely on professional judgment, and that such reliance is both necessary and desirable. Admittedly, there will likely be few situations where compliance with GAAP would result in misleading financial statements and the discretion to depart from GAAP can lead to abuses that, at the very least, would hamper the comparability of financial statement information. However, the requirement to verify that the application of GAAP results in fair presentation is an important safeguard given the complexity of the financial reporting environment.
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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 | 0.050 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.059 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".