Financial Reporting for Private Companies: The Canadian Experience*
Bibliographic record
Abstract
ABSTRACT The issue of whether small and/or private companies should be allowed to use simplified accounting standards in financial reports has concerned the accounting profession for decades. It has been argued that preparing financial reports in accordance with the large volume of promulgated standards contained in generally accepted accounting principles (GAAP), some of which are relatively complex, has put a significant strain on the resources of small/private business. Moreover, information produced and presented in accordance with at least some accounting standards within GAAP may not be relevant for the users of small/private companies' financial statements. In this paper we look at differential reporting in Canadian GAAP, which gives nonpublicly accountable enterprises the ability to opt out of certain CICA Handbook requirements with unanimous consent of the shareholders. We look at lobbying activity in response to the proposed differential reporting standard and at nonpublicly accountable company experience with the differential reporting opportunity in the period since the standard was promulgated.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".