The Effects of Various Choices of IFRS Implementation on the Relevance of Accounting Information
Bibliographic record
Abstract
To adopt IFRS, some jurisdictions as South Africa has chosen a method of implementation IFRS by the adoption of the IASB process while others like Canada, South Korea and the European Union countries opted for outstanding by standard method to implement IFRS. China preferred an incomplete transposition of these standards to the international reference. Faced with these various choices, the question of the impact of the IFRS implementation on the relevance of accounting information remains posed. To answer this question, we have studied the evolution of the relevance of accounting information following the adoption of the IFRS implementation method for a sample composed of listed companies. These firms are from six countries which have opted for different implementation methods of IFRS. The results show a maintain of the high relevance of accounting information for South Africa which opted for an implementation process and an improvement of the relevance for countries which has adopted a standard by standard method of IFRS implementation such as Canada, Germany and Spain excepting South Korea. In China, which conducted an extensive but incomplete convergence of its standards to IFRS, the results demonstrate a decrease in the relevance of the accounting information. Also, comparative analysis of the results shows a better relevance of accounting information for countries which have opted for methods of implementation fairly close to the IFRS.
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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 | 0.020 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".