International Financial Reporting Standard (IFRS): Benefits, Obstacles And Intrigues For Implementation In Nigeria
Bibliographic record
Abstract
IFRS is International Financial Reporting System and International Accounting Standard Board – (IASB) provided the framework for its working. IFRS adopted by IASB has gained worldwide acceptance amongst many countries and some listed companies in European nations have embraced it. IFRS employs a uniform, single consistent accounting framework that is gravitating towards General Accepted Accounting Practice (GAPP) in the future. IFRS since its introduction in 2001 had provided uniform accounting in financial reporting which would enable investors to interpreted financial statements with minimum effort. Other countries, including Canada and India are expected to transit to IFRS by 2011. The Nigerian Accounting Standard Board (NASB) is not expected to lag behind in the implementation. This paper looks at the benefits of adopting IFRS, obstacles and intrigues expected from the implementation of IFRS. The article also analyzed the requirements that would assists in the implementation of IFRS in Nigeria. Using content analysis method, the paper amongst others recommended a continuous research in order to harmonize and converge with the international standards through mutual international understanding of corporate objectives and the building of human capacity that will support the preparation of financial statements in organization.
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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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".