A Perspective on the Canadian Accounting Standards Board Exposure Draft on Generally Accepted Accounting Principles for Private Enterprises
Bibliographic record
Abstract
SYNOPSIS: The Canadian Accounting Standards Board (hereafter, AcSB) recently issued an exposure draft to adopt separate GAAP for private enterprises. This new GAAP is justified as being consistent with the current FASB/IASB conceptual framework, but is sensitive to the different cost-benefit considerations facing private entities. We view this proposal as being innovative and responsive to the differential reporting needs of private entities. In this article we explain our reasoning and conclusions on several issues raised by the exposure draft starting with a discussion about the need for a separate conceptual framework for private enterprises. We sketch a preliminary conceptual framework that could be used to develop and justify the type of changes proposed in this exposure draft. We then discuss key issues raised in the exposure draft such as reliance on historical cost as the key basis of measurement, the significant reduction in disclosure requirements for private enterprises, and stopping the emerging issues committee from providing implementation guidance (no EICs). We also comment on the mechanism for financing the standard-setting board, the need to ensure compatibility between accounting and auditing standards, and a process for adjusting the education system to support this new private enterprise GAAP.
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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 | 0.071 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.029 | 0.007 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.029 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 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".