Response to the Financial Accounting Standards Board’s Exposure Draft “Income Statement—Reporting Comprehensive Income—Expense Disaggregation Disclosures (Subtopic 220-40)”
Bibliographic record
Abstract
ABSTRACT The Financial Accounting and Reporting Section of the American Accounting Association has charged the Financial Reporting Policy Committee with responding to requests for comment from standard setters on issues related to financial reporting. The Committee is pleased to respond to proposed disclosure amendments in the FASB’s Exposure Draft, Disaggregation of Income Statement Expenses. The proposed disclosures in the Exposure Draft are intended to help investors better understand the components of an entity’s expenses and overall performance. The Committee evaluated the Exposure Draft and believes that, overall, the proposed amendments will provide decision-useful information to investors by enhancing their ability to evaluate and predict an entity’s performance. However, the Committee has several specific recommendations that we believe will better meet the objectives of the Exposure Draft. We summarize relevant findings from academic research to support our conclusions and recommendations. We also offer suggestions for future research. JEL Classifications: M41; M48.
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.
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 | 0.048 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.040 | 0.019 |
| Insufficient payload (model declined to judge) | 0.060 | 0.053 |
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".