Proposals to Change Lease Accounting: Evidence from Canada and Malaysia
Bibliographic record
Abstract
Leasing transactions are significant in business activities and both national and international accounting regulations require leases to be classified as either operating or finance leases. The International Accounting Standards Board recently proposed for the present classification to be removed, and for both finance and operating leases to appear on the balance sheet. This paper compares the opinions of 63 qualified accountants in Canada and 54 qualified accountants in Malaysia on the present regulations and the implications of the new standard. The responses from these countries support the substance-over-form model, but raise doubts on its applicability to leasing transactions. The majority of respondents agree on one method for accounting for leases, but support for the removal of finance and operating lease classifications is weaker. An analysis of the data reveals that those who believe the current information is of use are more likely to reject the proposed changes. This suggests that future research should be less concerned with whether users find the information relevant and should be directed towards the nature of user decisions and how the present information is utilised.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".