The change in the useful life of an intangible asset: prospectively, retrospectively or otherwise?
Bibliographic record
Abstract
Over the past more than a quarter of a century of active reform of Russian accounting in accordance with IFRS, half of this period there was no such thing as a “change in estimated values” in regulatory documents. In accordance with PBU 21/2008 “Changes in estimated values”, changes in estimated values are reflected in accounting and reporting prospectively. As practice shows, not only practitioners, but also some theorists do not always understand this perspective order unambiguously and correctly. In this article, the prospective procedure is considered by the example of changing the useful life of intangible assets. When conducting research, we used such methods as analysis and synthesis, comparison, grouping method, analogy method, logical approach, system approach. As a result of the conducted research, this article, in particular, presents various approaches to making changes to accounting and reporting in connection with the adoption of new accounting standards close to IFRS. In addition, in this paper, proposals are made to improve the accounting standard to reflect the consequences of changes in estimated values. The results of this study will be of interest to a wide range of readers studying various problems of modern Russian and foreign accounting and reporting, and can also be used in the educational process of higher educational institutions.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".