Bibliographic record
Abstract
Abstract The increase in regulation has caused the number of publicly traded Canadian public companies to decline. This study analyzes audit fee data from Audit Analytics from 2002 through 2015 for Canadian companies to determine if they have moved away from using Big 4 auditors. Charts and regression are used to analyze the data. Results show that there is a decline in the number of public audits from 2002 to 2015 that seems to correspond to the decline of Canadian public companies due to increased regulation. In spite of the decline in the number of publicly traded companies, the results of this study show that, of the total audits, the proportion done by Big 4 auditors is increasing, possibly due to the need for higher audit quality and more experienced auditors for the companies to be listed on stock exchanges. This leads to higher audit fees. As a result, many executives have decided to fund their companies with private equity. This decline in the number of publicly traded companies is not healthy for the Canadian market. Perhaps more incentives can be given to attract more public issuers, which will give investors more opportunities to invest, and thus, further strengthen the economy.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".