A Review of IFRS and U.S. GAAP Convergence History and Relevant Studies
Bibliographic record
Abstract
In this paper, I conduct a review of the IFRS and U.S. GAAP convergence history and the related studies. I first review the history of the early accounting standards harmonization efforts in the Europe Union in the 1970s and 1980s. Next, I discuss the modern convergence efforts as well as the voluntary adoption of the International Accounting Standards (IAS) in the 1990s and early 2000s. I then discuss the concurrent global accounting standards convergence efforts from 2002 to present, including the mandatory IFRS adoption in the EU in 2005 and the convergence between the IFRS and U.S. GAAP. As I review the harmonization and convergence efforts over time, I discuss studies related to each stage of the history. I contribute to the accounting literature by providing a historical review of the IFRS-U.S. GAAP convergence process and relevant studies that can be useful for accounting educators and students, researchers, practitioners, and standards setters.
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How this classification was reachedexpand
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 | 0.002 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".