VOLUNTARY DISCLOSURE PRACTICES: THE USE OF PRO FORMA REPORTING
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
This article looks at how U.S. managers supplement GAAP earnings with pro forma reporting. Pro forma measures, which are not audited, are typically determined through an adjustment to GAAP‐based earnings. For example, a manager may choose to present an alternative to GAAP earnings that excludes period write‐offs and one‐time restructuring charges in order to present a more value‐relevant picture of the company's performance. The authors find that 77% of S&P 500 companies report pro forma results, and that pro forma measures are generally given greater prominence than GAAP earnings in corporate press releases. Based on the evidence, U.S. managers are using pro forma reporting strategically to affect investor perception of corporate performance. The SEC has recently issued rules to ensure that pro forma disclosure is not misleading. The authors present some guidelines on voluntary disclosure that might help forestall further regulation and preserve the ability to pursue this potentially informative practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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