Going Concern Designations and GAAP versus Non-GAAP Earnings Metrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT: Many students have not spent much time studying or contemplating the importance of non-GAAP (Generally Accepted Accounting Principles) earnings to the “Street.” Based on the facts of an actual company and utilizing the financial information drawn from this company's 10-K and Earnings Release, this case introduces students to the strengths and weaknesses of GAAP and non-GAAP earnings measures, and why the Street might be more interested in cash and recurring earnings in attempting to predict movements in stock price. It also provides the instructor with an opportunity to discuss the dangers of allowing firms to emphasize earnings in their press releases that are not defined by an external authoritative body (such as the Financial Accounting Standards Board [FASB]), and how this can hurt the consistency and reliability of reporting. This is an important discussion, since regulators have recently formally proposed to include non-GAAP measures in their overhaul of the auditor reporting model (Public Company Accounting Oversight Board [PCAOB] 2011). The case also familiarizes students with current auditing guidelines dealing with the going concern decision and the potential role that non-GAAP earnings can play in this decision. Thus, the three primary learning objectives are to teach students: (1) to apply going concern audit standards, (2) about the potential role of non-GAAP earnings in this decision—especially as a predictor of future cash flows, and (3) other issues associated with non-GAAP earnings. This topic is important, as auditors are frequently auditing companies that release non-GAAP earnings and/or have going concern issues. This case can be used in Intermediate and Auditing classes, as well as master's-level courses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it