The FASB's Conceptual Framework for Financial Reporting: A Critical Analysis by the American Accounting Association's Financial Accounting Standards Committee
Bibliographic record
Abstract
This paper addresses the issues that confront the FASB and IASB in developing a new conceptual framework document. First, we suggest characteristics that a conceptual framework ought to exhibit. Most of these suggestions are based on our critique of the existing framework and the FASB-IASB work in progress. Second, we present a model framework that exhibits these characteristics. We emphasize up front that this framework is quite explicit. It goes to the heart of what a framework document should do: it places specific restrictions on what constitutes admissible accounting standards. The purpose of our effort is to stimulate broad discussion of alternative approaches to foundational documents and to offer a specific example of such an alternative approach.
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 | 0.109 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".