The Central Florida Emphysema Foundation Audit: A Case Study of Personal and Professional Responsibility
Bibliographic record
Abstract
ABSTRACT The in-charge accountant (ICA) for the Central Florida Emphysema Foundation (CFEF) audit engagement is left to wrap up the audit while the audit manager is away on vacation and the audit partner unexpectedly leaves for an out-of-state family funeral. Only one outstanding issue remains—accounting for a $5,000,000 cash bequest that CFEF received in the mail shortly after year-end. What is the appropriate accounting? After working through the issue, the ICA ends up on the opposite side of the fence from the client and even an audit partner from an associated firm. What should the ICA do? This instructional case, based on a real-life experience, provides students the opportunity to gain a better understanding of an auditor's professional responsibilities through examination of the issues that arise in the audit of a not-for-profit entity. The case focuses students on important attributes one needs to be a successful CPA—ethics and integrity, perseverance, sound judgment, and decision-making and professional skepticism. Applying their knowledge of GAAP, auditing standards and the American Institute of Certified Public Accountants' [AICPA] (2010) Code of Professional Conduct, students will gain a better understanding of the types of situations that arise in practice and will confront the personal and professional choices that auditors must make.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".