Simulation of the Impact of the Recognition of Stock Options on the Earnings: The case of Canadian Companies*
Bibliographic record
Abstract
One of the most controversial accounting issues pertains to stock compensation. In Canada, the Canadian Institute of Chartered Accountants (CICA) approved section 3870, Stock-based Compensation and Other Stock-Based Payments, on November 13, 2001, to take effect in January 2002. Section 3870 forces companies to “take a look at the real economic cost of most of the stock-based compensation mechanisms” (AcSB Bulletin, October 2001, 1). The adoption of section 3870 was aimed at harmonizing Canadian accounting practice with U.S. standards. The new standard, which was initially based on two American accounting standards - APB Opinion No. 25 and SFAS No. 123 - gave companies the choice of using either the fair value method or the pro forma disclosure of net income and adjusted earnings per share to account for stock-based compensation. The Accounting Standards Board (AcSB) nevertheless recommended that Canadian companies use the fair value method, which consists in estimating and recognizing the value of the stock options at the grant date.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".