Gaap vs. Ifrs Treatment of Leases and the Impact on Financial Ratios
Bibliographic record
Abstract
As of January 1, 2011, most of the world financial market economies are using International Reporting Standards (IFRS) as the required framework for financial statements. A non-comprehensive listing includes the European Union Countries, Canada, Australia and New Zealand. In the United States, US Generally Accepted Accounting Principles (GAAP) is still required but adoption of IFRS has support of many accounting firms and professional organizations and is under consideration by the SEC. This case study focuses on differences in the treatment of leases and the impact of these differences on financial statements and selected financial ratios. Students take GAAP financial statements and prepare an IFRS based balance sheet and income statement. It is necessary to understand both GAAP and IFRS rules regarding leases to address this case study. This case study is suitable for use at both the undergraduate and graduate levels. It may be used in an Intermediate Accounting II, Accounting Theory, Financial Statement Analysis or an International Accounting class, as well as an Investment Finance course. The case can be offered as an individual case study or as a group project.
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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".