Bibliographic record
Abstract
Purpose: This paper purpose is to report the differences between U.S. GAAP and IFRS by presenting a review of literature available on the topic. Methodology: This paper is based on review of 27 research papers. This paper is divided into two parts. First part presents the previous studies focused on business aspects (companies can ill afford the cost of an increasing number of GAAP standards and impact on investors) and second part presents the previous studies focused on fundamental differences between U.S. GAAP and IFRS. Findings: This paper is based on review of literature that present the differences between U.S. GAAP and International Financial Reporting Standards (IFRS). As per the studies done, U.S. GAAP contains more detailed, specific requirements than IFRS. In some instances, IFRS does not contain any corresponding guidance and, in others, IFRS contains higher-level or general guidance that is not directly comparable to the U.S. GAAP requirement. Originality/Value: This paper findings are based on 27 research papers only. As accounting standards are changing due to change in current economic situations, there is a large scope for the future studies based on U.S. GAAP and IFRS differences and impact of those difference on the investors.
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 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".