Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate whether reported net income as per US‐generally accepted accounting principles (US‐GAAP) has become comparable to net income as per International Financial Reporting Standards (IFRS) as issued by the International Accounting Standards Board right before the removal of the US‐GAAP reconciliation requirement and what major accounting elements have caused the differences, if any. Design/methodology/approach Using Gray's index of comparability suggested by Haverty, the paper compares the reported net income under IFRS for a sample of US‐listed Chinese companies using IFRS with the reconciled net income under US‐GAAP. Findings Consistent with Haverty is the finding that net income under IFRS is still not completely comparable to net income under US‐GAAP for the same company and that the adjustment for tangible assets revaluation is a major contributor to the difference. In addition, different treatment of business acquisition is found to be another major cause of the incomparability. The comparability has improved at 10 percent threshold since Haverty's study. Originality/value This paper provides an update on the status of IFRS and US‐GAAP comparability and highlights an additional major area to work on towards improved comparability.
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 | 0.035 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".