Bibliographic record
Abstract
An Executive's Guide for Moving from US GAAP to IFRS reviews different issues relating to the possibility that the Securities and Exchange Commission (SEC) may eventually mandate the use of International Financial Reporting Standards (IFRS) for use by listed companies and delegate to the International Accounting Standards Board (IASB) the task of providing accounting standards for the United States. The first chapter reviews the international movement to converge on a single global basis of accounting for listed companies. It also discusses the experience of European companies, where 25 countries adopted IFRS in 2005. The second chapter analyzes the position in the United States. It looks at the advantages and disadvantages for corporations and explains the convergence program being followed by the Financial Accounting Standards Board (FASB) and the IASB. It also looks at the SEC’s activities in this area and then sets out the challenges to be addressed by U.S. corporations if IFRS are adopted. Canada has made the decision to switch in 2011, and the Canadian experience is discussed as offering a blueprint for the United States. This is followed by an extensive analysis of the technical differences between IFRS and U.S. Generally Accepted Accounting Principles (GAAP). The last two chapters explain the organizational structure of the IASB and its standard-setting process, and then the evolution of the international standard-setter from its beginning in 1973.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.332 | 0.424 |
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".